├── Linkden ├── New Text Document (15).txt └── How to analyse your LinkedIn profile with Claude..md └── README.md /Linkden/New Text Document (15).txt: -------------------------------------------------------------------------------- 1 | ahmed.sayed@qmul.ac.uk -------------------------------------------------------------------------------- /Linkden/How to analyse your LinkedIn profile with Claude..md: -------------------------------------------------------------------------------- 1 | How to analyse your LinkedIn profile with Claude. 2 | 3 | 1. Download Claude for Chrome: [chromewebstore.google.c](https://chromewebstore.google.com/detail/claude/fcoeoabgfenejglbffodgkkbkcdhcgfn) 4 | 5 | 2. Open LinkedIn + Claude for Chrome. 6 | 7 | 3. Paste in this prompt: 8 | 9 | 4. ### META PROMPT: LinkedIn Profile Analysis (Based on Charlie Hills Framework) 10 | 11 | You are an expert LinkedIn brand strategist. 12 | 13 | Analyse the given LinkedIn profile step-by-step using the framework below. 14 | 15 | Provide concise, actionable feedback for each section. 16 | 17 | Highlight missing elements, weak phrasing, or design issues. 18 | 19 | Use bullet points, short sentences, and tactical advice. 20 | 21 | Be specific, not generic. 22 | 23 | --- 24 | 25 | ### FRAMEWORK FOR ANALYSIS 26 | 27 | #### 1. PROFILE PICTURE 28 | 29 | - Is it zoomed in, 400×400 pixels, filling ~80% of frame? 30 | 31 | - Does it use brand colours and show a natural smile? 32 | 33 | - Is lighting consistent with brand visuals? 34 | 35 | #### 2. BANNER 36 | 37 | - Is there a clear CTA (top-left, not blocking photo)? 38 | 39 | - Does it state the offer in 5–7 words? 40 | 41 | - Does it include proof (logos, wins, follower count)? 42 | 43 | - Is the size correct (1584×396 pixels)? 44 | 45 | - Is the bottom-left corner left clear for the profile picture? 46 | 47 | #### 3. HEADLINE 48 | 49 | - Is it short and keyword-first (~40–45 visible chars on mobile)? 50 | 51 | - If short (≤70 chars): is it punchy and clear? 52 | 53 | - If long (71–220 chars): does it add proof or specificity? 54 | 55 | - Does it follow “I help [who] [do what] [how]”? 56 | 57 | - Is it free from buzzwords or fluff? 58 | 59 | #### 4. FEATURED SECTION 60 | 61 | - Uses 1200×628 px visuals? 62 | 63 | - Highlights a core offer or lead magnet? 64 | 65 | - Limited to 2–3 tiles max? 66 | 67 | - Does it drive traffic (newsletter, call, or resource)? 68 | 69 | - Is there visual and contextual alignment with the headline? 70 | 71 | #### 5. ABOUT SECTION 72 | 73 | - Uses storytelling (Hook → Struggle → Transformation → Mission)? 74 | 75 | - Includes measurable proof or achievements? 76 | 77 | - Contains a clear CTA (“Visit my newsletter”, “Book a call”, etc.)? 78 | 79 | - Does it sound personal and authentic rather than corporate? 80 | 81 | #### 6. CUSTOM LINK 82 | 83 | - Is there a premium custom link under the headline? 84 | 85 | - Does it drive to a key asset (newsletter, site, booking page)? 86 | 87 | - Is the CTA clear and benefit-driven? 88 | 89 | #### 7. EXPERIENCE 90 | 91 | - Does each role include storytelling and outcomes? 92 | 93 | - Are key external links (site, booking, case studies) added? 94 | 95 | - Are irrelevant or outdated roles removed? 96 | 97 | - Are all roles consistent with the personal brand or niche? 98 | 99 | #### 8. SKILLS 100 | 101 | - Are top two skills pinned and relevant to current positioning? 102 | 103 | - Are all 50 used to boost search visibility? 104 | 105 | - Are endorsements balanced and credible? 106 | 107 | --- 108 | 109 | ### OUTPUT FORMAT 110 | 111 | Section: [e.g. Headline] 112 | 113 | ✅ Strengths: 114 | 115 | - [Point 1] 116 | 117 | - [Point 2] 118 | 119 | ⚠️ Improvements: 120 | 121 | - [Point 1] 122 | 123 | - [Point 2] 124 | 125 | 🎯 Recommendation: [1-sentence actionable fix] 126 | 127 | 128 | ## 📑 Table of Contents 129 | 130 | - [What is Sentiment Analysis](#What-is-Sentiment-Analysis) 131 | - [How does sentiment analysis work](#How-does-sentiment-analysis-work) 132 | - [Supervised Machine Learning](#Supervised-Machine-Learning) 133 | - [Logistic Regression and Sentiment Analysis?](#Logistic-Regression-and-Sentiment-Analysis?) 134 | - [Challenges and Limitations](#Challenges-and-Limitations) 135 | - [Conclusion](#Conclusion) 136 | 137 | 138 | ### **What is Sentiment Analysis** 139 | Sentiment analysis is a natural language processing (NLP) technique that aims to understand and categorize the sentiment expressed in a given text. It involves analyzing the words, phrases, and context of the text to determine whether the sentiment is positive, negative, or neutral. 140 | 141 | Sentiment analysis, also known as opinion mining, is a technique used to determine the sentiment or emotion expressed in a piece of text. It has gained significant popularity in recent years due to its applications in various fields such as marketing, customer feedback analysis, and social media monitoring. In this blog post, we will explore the concept of sentiment analysis and delve into the details of using logistic regression as a powerful tool for sentiment classification. 142 | 143 | In this section, we will provide an overview of sentiment analysis and its importance in today’s data-driven world. 144 | 145 | Sentiment analysis is the process of extracting subjective information from text and determining the sentiment or emotion associated with it. It involves analyzing the text to classify it into positive, negative, or neutral sentiment categories. The main goal of sentiment analysis is to understand the opinions, attitudes, and emotions expressed by individuals or groups. This information can then be used to make informed decisions, improve customer service, and gain valuable insights. 146 | 147 | **Def:** Sentiment analysis has been widely used since the early 20th century, and its research area is still fast growing. One of the most advanced solutions is to use AI to proceed with sentiment analysis. The algorithm uses a natural language processing (NLP) technique which enables it to determine the moods or emotions of a piece of text. In this case, companies can react based on user feedback. 148 | 149 | **Def:** Sentiment analysis is a Natural Language Processing (NLP) [2] technique used to determine the sentiment of a text by automatically identifying its underlying opinions. The sentiment can be positive (e.g. “I’m very happy today”), negative (e.g. “I didn’t like that movie”), or neutral (e.g. “Today is Friday”, which may be subjectively seen as positive by some people actually 😁) [1] 150 | 151 | ## **How does sentiment analysis work** 152 | 153 | Sentiment analysis typically works by first identifying the sentiment of individual words or phrases. This can be done using a variety of methods, such as **lexicon-based analysis**, **machine learning**, or **natural language processing**. 154 | 155 | Once the sentiment of individual words or phrases has been identified, they can be combined to determine the overall feeling of a piece of text. This can be done using a variety of techniques, such as **sentiment scoring** or **sentiment classification**. 156 | 157 |

158 | 159 |

160 | 161 | Background: machine-learning classification task of sentiment analysis. In this example you have the tweet, let’s say, I’m happy because I’m learning NLP. And the objective in this task is to predict whether a tweet has a positive or negative sentiment. And you’ll do this by starting with a training set where tweets with a positive sentiment have a label of one, and the tweets with a negative sentiment have a label of zero. 162 | 163 | 164 | In order for you to implement logistic regression, you need to take a few steps. In this tutorial you will learn about the steps required in order to implement this algorithm, so let’s take a look. 165 | 166 | ## **Supervised Machine Learning** 167 | 168 | In supervised machine learning, you have input features X and a set of labels Y. Now to make sure you’re getting the most accurate predictions based on your data, your goal is to minimize your error rates or cost as much as possible. And to do this, you’re going to run your prediction function which takes in parameters data to map your features to output labels Y hat. 169 | 170 | Now the best mapping from features to labels is achieved when the difference between the expected values Y and the predicted values Y hat is minimized. Which the cost function does by comparing how closely your output Y hat is to your label Y. Then you can update your parameters and repeat the whole process until your cost is minimized. So let’s take a look at the supervised 171 | 172 |

173 | 174 |

175 | 176 | 177 | ## **Logistic Regression and Sentiment Analysis**? 178 | 179 | Logistic regression is a statistical model used to predict binary outcomes. It is commonly used when the dependent variable is dichotomous, meaning it can take only two values. In the context of sentiment analysis, the binary outcome represents the sentiment category (positive or negative). 180 | 181 | Logistic regression is a popular machine learning algorithm used for binary classification problems. It is well-suited for sentiment analysis because it can handle text data and provide probabilistic outputs. Logistic regression models are interpretable and can capture nonlinear relationships between features and labels. 182 | 183 | Logistic regression works by estimating the probability of an event occurring based on a set of independent variables. It uses a logistic function (also known as a sigmoid function) to map the linear combination of the independent variables to a value between 0 and 1. This value represents the probability of the event occurring. 184 | 185 | For this task you will be using your logistic regression classifier which assigns its observations to two distinct classes. Next up I’ll show you how to do this. So to get started building a logistic regression classifier that’s capable of predicting sentiments of an arbitrary tweet. 186 | 187 | 188 |

189 | 190 |

191 | 192 | You will first process the raw tweets in your training sets and extract useful features. Then you will train your logistic regression classifier while minimizing the cost. And finally you’ll be able to make your predictions. So in this blog you learned about the steps required for you to classify a tweet. Given the tweet, you should classify it to either be positive or negative. In order for you to do so, you first have to extract the features. Then you have to train your model. And then you have to classify the tweet based off your trained model. In the next video, you’re going to learn how to extract these features. So let’s take a look at how you can do that 193 | 194 | ### **Preparing the Data** 195 | Before we can build a sentiment analysis model, we need to prepare the data. This involves cleaning and preprocessing the text, as well as labeling the data with sentiment labels (positive, negative, or neutral). 196 | 197 | ### **Feature Extraction** 198 | Feature extraction is a crucial step in sentiment analysis. It involves converting the text into numerical features that can be used by a machine learning model. Some common feature extraction techniques for sentiment analysis include 199 | - bag-of-words, 200 | - TF-IDF 201 | - word embeddings. 202 | 203 | ### **Building the Logistic Regression Model** 204 | 205 | Once we have extracted the features, we can build our logistic regression model. We will use the scikit-learn library in Python to implement logistic regression. This involves splitting the data into training and testing sets, fitting the model on the training data, and evaluating its performance on the testing data. 206 | 207 | ### **Evaluating the Model** 208 | 209 | To evaluate the performance of our sentiment analysis model, we can use various metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into how well our model is performing in classifying sentiment. 210 | 211 | ### **Improving the Model** 212 | 213 | There are several ways to improve the performance of our sentiment analysis model. One approach is to experiment with different feature extraction techniques and see which one works best for our dataset. We can also try using more advanced machine learning algorithms or ensemble methods to improve accuracy. 214 | 215 | ### **Real-World Applications** 216 | 217 | Sentiment analysis has a wide range of real-world applications. It can be used in social media monitoring to analyze customer opinions and feedback. Companies can use sentiment analysis to understand customer satisfaction and make informed business decisions. Sentiment analysis can also be applied in product reviews, brand monitoring, and market research. 218 | Sentiment analysis has several applications, such as: 219 | 220 | **Understanding customer sentiment** in social media, product reviews, and survey responses to find out what customers think about your products and services, and to make improvements accordingly. 221 | 222 | **Automatically generating product** recommendations based on users' sentiment towards them. 223 | Identifying influencers who have a positive or negative influence on public opinion and who may be relevant for advertising your products. 224 | Tracking the sentiment of a brand or product over time to improve the brand or adjust marketing efforts. This type of analysis can be done on competitors as well. 225 | 226 | **Monitoring employee morale**. This information can be useful to managers as it helps them identify problem areas that may need to be addressed. It can also help them see how employees are responding to changes within the company, such as new policies or initiatives. 227 | 228 | ## **Challenges and Limitations** 229 | 230 | While sentiment analysis has proven to be effective in many cases, it does have its limitations. One major challenge is dealing with sarcasm and irony in text, as these can often lead to misinterpretation of sentiment. Sentiment analysis models may also struggle with domain-specific language or slang. Additionally, sentiment analysis is subjective and can vary based on cultural differences and individual interpretations. 231 | 232 | ## **Conclusion** 233 | In conclusion, sentiment analysis using logistic regression is a powerful technique for understanding the sentiment expressed in text data. By preprocessing the data, extracting relevant features, and building a logistic regression model, we can accurately classify sentiment as positive, negative, or neutral. Sentiment analysis has numerous applications in various industries and can provide valuable insights for decision-making processes. However, it is important to be aware of its limitations and challenges in order to obtain reliable results. 234 | 235 | ### References 236 | 237 | 1-[public pre-trained models for sentiment analysis on Hugging Face.](https://huggingface.co/models?search=sentiment) 238 | 239 | 2-[Two minutes NLP — Quick Intro to Sentiment Analysis](https://medium.com/nlplanet/two-minutes-nlp-quick-intro-to-sentiment-analysis-106b6947b2fd) 240 | 241 | 4-[Understanding the Emotion Tone of Text with AI — Sentiment Analysis on Monkeypox Tweets](https://pub.towardsai.net/understanding-the-emotion-tone-of-text-with-ai-sentiment-analysis-on-monkeypox-tweets-13040cfb1f99) 242 | 243 | 5-[Sentiment Analysis: Marketing with Large Language Models (LLMs)](https://medium.com/codex/computer-vision-fundamentals-with-opencv-9fc93b61e3e8) 244 | 245 | 246 |

247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | 265 | 266 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 276 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 292 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 |

3 | Awesome Job Search& Resources 4 |
5 |

6 | 7 |

8 | 🏆  A ranked list of awesome Job Websites. Updated weekly. 9 |

10 | 11 | This curated list showcases some of the best and most reliable job websites—categorized by job type, industry, and location. Whether you're a fresh graduate, experienced professional, or freelancer, these platforms can help you find the right opportunities faster. 12 | 13 | If you’d like to suggest a job site or contribute to this directory, feel free to reach out or submit ideas via pull request. 14 | 15 | 16 | ## 🤝 Contributing 17 | 18 | We welcome suggestions and contributions to this list of awesome AI tools! 19 | 20 | ### ✅ How to Contribute 21 | 22 | 1. **Fork** this repository. 23 | 2. **Add your tool** in the appropriate category (alphabetically, if possible). 24 | 3. Include the following details: 25 | - Tool **Name** (with link) 26 | - A **short description** (15–20 words) 27 | - Relevant **Tags** (e.g., `Chatbot, Free, Web`) 28 | 4. Create a **Pull Request (PR)** with a clear title and description of your changes. 29 | 30 | --- 31 | 32 | ### 💡 Alternative: Suggest via GitHub Issues 33 | 34 | If you're not familiar with Git or Pull Requests, you can still contribute: 35 | 36 | - Open a **GitHub Issue** with the following format: 37 | 38 | Tool Name: Tool Title 39 | Description: One-line summary of what it does. 40 | Suggested Category: e.g., Video Tools, Chatbots 41 | Tags: Free, Web, Chatbot 42 | 43 | --- 44 | 45 | ### 📫 Contact 46 | 47 | For bulk suggestions, feedback, or collaboration, feel free to [open an issue](https://github.com/your-repo/issues) or reach out via email/LinkedIn. 48 | 49 | 50 | 51 | 52 | 53 | ## 📬 Stay Updated with Weekly AI Tools List! 54 | 55 | Never miss a tutorial! Get weekly insights, updates, and bonus content straight to your inbox. 56 | **Join hundreds of AI tool users on Substack.** 57 | 58 | 👉 [**Subscribe to Our Coursesteach Newsletter**](https://substack.com/@coursesteach) ✨ 59 | 60 | [![Subscribe on Substack](https://img.shields.io/badge/Subscribe-Substack-orange?style=for-the-badge&logo=substack)](https://substack.com/@coursesteach) 61 | 62 | Never miss a tutorial! Get weekly insights, updates, and bonus content straight to your inbox. 63 | **Join hundreds of AI tool on Substack.** 64 | 65 | 👉 [**Subscribe to Our Coureseateach Newsletter**](https://substack.com/@coursesteach) ✨ 66 | 67 | 💡 Optional Badge (to make it pop) 68 | 69 | [![Subscribe on Substack](https://img.shields.io/badge/Subscribe-Substack-orange?style=for-the-badge&logo=substack)](https://substack.com/@coursesteach) 70 | 71 |

72 | 73 | --- 74 | 75 | 76 | ## Contents 77 | 78 |
79 |

📄 Resume & Cover Letter Tools

80 | 81 | | Title/Link | Description | Tags | 82 | |---|---|---| 83 | | [**Kickresume**](https://www.kickresume.com/) | AI-powered resume builder with templates. | Resume, AI, Free/Paid | 84 | | [**ResumeWorded**](https://resumeworded.com/) | Improves your resume with AI suggestions. | Resume, AI, Free Tier | 85 | | [**Canva Resume Builder**](https://www.canva.com/resumes/) | Professional resume templates. | Resume, Design, Free | 86 | | [**CoverDoc.ai**](https://coverdoc.ai/) | Generates personalized cover letters. | Cover Letter, AI, Free | 87 |
88 | 89 |
90 |

🔍 Globel Job Search Platforms

91 | 92 | | Title/Link | Description | Tags | 93 | |---|---|---| 94 | | [**LinkedIn Jobs**](https://www.linkedin.com/jobs/) | Largest professional job board. | Jobs, Networking, Free | 95 | | [**Indeed**](https://www.indeed.com/) | Comprehensive job search engine. | Jobs, Aggregator, Free | 96 | | [**AngelList**](https://angel.co/jobs) | Startup job opportunities. | Jobs, Startups, Free | 97 | | [**RemoteOK**](https://remoteok.com/) | Remote job listings. | Jobs, Remote, Free | 98 | | [**laboro**](https://laboro.co/search) | AI Agent to Auto-Apply ML Jobs | Jobs, AI job, Free | 99 | | [**work.mercor**](https://work.mercor.com/explore) | Mercor is an AI-powered recruiting and freelance work platform, especially prominent in the AI, data annotation, and intelligence engineering fields. You create an account once, upload your resume, and Mercor matches you to thousands of job opportunities globally | Jobs, AI job, Free | 100 | | [**tudelft**](https://careers.tudelft.nl/go/All-jobs/9021002/) | Find Postdoc and others job | Jobs, AI job, Free,Postdoc,PhD | 101 | | [**sciencecareers**](https://jobs.sciencecareers.org/) | Jobs in Science & Technology from Science Careers| Jobs, AI job, Free,Postdoc,Ph | 102 |
103 | 104 |
105 |

🔍 Globel Job Search Social media Group and Sheet

106 | 107 | | Title/Link | Description | Tags | 108 | |---|---|---| 109 | | [**Post doc**](https://chat.whatsapp.com/HN3oPF1YUbjGtajf3oyJf8) | Find Postdoc Whatsapp Group| Jobs, Postdoc Whatsapp | 110 | | [**Postdoc and Phd**](https://docs.google.com/spreadsheets/d/1YhOUW_kUtm1Ju2Mc69_15Afvl9ApXXYJhd4iVnUpBr4/edit?gid=497142877#gid=497142877) | Google Sheet for Share Postdoc and PhD job | Jobs, Google Sheet | 111 | 112 |
113 | 114 |
115 |

💼 Interview Preparation

116 | 117 | | Title/Link | Description | Tags | 118 | |---|---|---| 119 | | [**Pramp**](https://www.pramp.com/) | Free mock technical interviews. | Interview, Tech, Free | 120 | | [**Interviewing.io**](https://interviewing.io/) | Anonymous technical interview practice. | Interview, Tech, Free Tier | 121 | | [**Big Interview**](https://www.biginterview.com/) | AI-powered interview coaching. | Interview, AI, Paid | 122 | | [**prachub**](https://prachub.com/questions) | AI-powered interview coaching. | Interview, AI, Paid | 123 |
124 | 125 |
126 |

🌐 Networking Tools

127 | 128 | | Title/Link | Description | Tags | 129 | |---|---|---| 130 | | [**LinkedIn**](https://www.linkedin.com/) | Essential professional networking. | Networking, Free | 131 | | [**Shapr**](https://www.shapr.com/) | Networking app for professionals. | Networking, Free | 132 | | [**Lunchclub**](https://lunchclub.com/) | AI-matched professional meetings. | Networking, AI, Free | 133 |
134 | 135 |
136 |

📈 Job Tracker Tools

137 | 138 | | Title/Link | Description | Tags | 139 | |---|---|---| 140 | | [**Huntr**](https://huntr.co/) | Visual job application tracker. | Organization, Free | 141 | | [**Teal**](https://www.tealhq.com/) | All-in-one job search manager. | Organization, Free | 142 | | [**JibberJobber**](https://www.jibberjobber.com/) | Career management CRM. | Organization, Free Tier | 143 | | [**searchlinkbuilder**](https://searchlinkbuilder.com/) | PSA because this is very handy - I found this very useful tool that builds the boolean operator links for you. Add your wants and don't wants. And then just copy and paste the link from the bottom.| Job, lInkden| 144 |
145 | 146 |
147 |

🔍 Postdoc Job Search Platforms

148 | 149 | | Title/Link | Description | Tags | 150 | |---|---|---| 151 | | [**LinkedIn Jobs**](https://www.linkedin.com/jobs/) | Largest professional job board. | Jobs, Networking, Free | 152 | | [**Talent**](https://au.talent.com/jobs?k=Postdoctoral+Fellow&l=Australia&date=3&id=8fdb8505dce2) | Find postdoc and other job austrial | austrial | 153 | | [**Indeed**](https://www.indeed.com/) | Comprehensive job search engine. | Jobs, Aggregator, Free | 154 | | [**AngelList**](https://angel.co/jobs) | Startup job opportunities. | Jobs, Startups, Free | 155 | | [**RemoteOK**](https://remoteok.com/) | Remote job listings. | Jobs, Remote, Free | 156 | | [**Euraxess**](https://lnkd.in/gV4_aEKi) | EU-funded portal for research and postdoc positions. | Europe, Research | 157 | | [**Nature Careers**](https://lnkd.in/gBAJYGuh) | Jobs and fellowships in science and academia. | Global, Science | 158 | | [**ResearchGate**](https://lnkd.in/gBKNn4-w) | Research-focused networking and job board. | Global, Research | 159 | | [**FindAPostDoc**](https://lnkd.in/gadznjrJ) | Dedicated platform for postdoc opportunities. | Global, Academia | 160 | | [**Academic Positions**](https://lnkd.in/gKfjZy9K) | International academic career network. | Global, Research | 161 | | [**PostdocJobs**](https://lnkd.in/gc4sJC4R) | Specialized portal for postdoc positions. | Global, Academia | 162 | | [**Scholarship Positions**](https://lnkd.in/gtQ2KJXN) | Database of scholarships and postdoctoral fellowships. | Global, Funding | 163 | | [**NIH Office of Intramural Training & Education**](https://lnkd.in/gN8iwysw) | Postdoc training and fellowships at NIH. | USA, Biomedical | 164 | | [**NSF Postdoctoral Fellowships**](https://lnkd.in/gn3ytcaT) | Competitive fellowships for postdoctoral research. | USA, Research | 165 | | [**Science Careers**](https://lnkd.in/gjfakgcd) | Research and postdoc jobs from AAAS. | USA, Science | 166 | | [**Harvard Postdoc**](https://lnkd.in/gm8FSV4v) | Postdoctoral opportunities at Harvard. | USA, Academia | 167 | | [**Stanford Postdoc**](https://lnkd.in/g2M7-eci) | Postdoc resources at Stanford University. | USA, Academia | 168 | | [**Berkeley Postdoc**](https://lnkd.in/gHGB7pXw) | UC Berkeley postdoctoral affairs. | USA, Academia | 169 | | [**Yale Postdoc**](https://postdocs.yale.edu/) | Yale University postdoctoral positions. | USA, Academia | 170 | | [**MIT Postdoc**](https://postdocs.mit.edu/) | MIT postdoctoral opportunities. | USA, Academia | 171 | | [**UC Postdoc**](https://postdocs.ucsd.edu/) | University of California postdoctoral programs. | USA, Academia | 172 | | [**Johns Hopkins Postdoc**](https://postdoc.jhu.edu/) | Postdoc opportunities at JHU. | USA, Academia | 173 | | [**Cornell Postdoc**](https://lnkd.in/gj8Tmvkr) | Postdoctoral positions at Cornell. | USA, Academia | 174 | | [**Canadian Association of Postdoctoral Scholars (CAPS)**](https://www.caps-acsp.ca/) | National association supporting postdocs in Canada. | Canada, Networking | 175 | | [**Banting Fellowships**](https://lnkd.in/gMEwsHjU) | Prestigious Canadian government fellowships. | Canada, Funding | 176 | | [**Mitacs Elevate**](https://lnkd.in/g6M-8TxS) | Postdoctoral fellowship program with industry links. | Canada, Research | 177 | | [**University of Toronto Postdoc**](https://lnkd.in/g_s2yzbp) | Postdoctoral fellowships at UofT. | Canada, Academia | 178 | | [**UBC Postdoc**](https://lnkd.in/gNrw4jjq) | Postdoc opportunities at UBC. | Canada, Academia | 179 | | [**UKRI**](https://lnkd.in/gHTR-hRm) | Research funding and fellowships in the UK. | UK, Funding | 180 | | [**The Royal Society Fellowships**](https://lnkd.in/gMCdvBhC) | Prestigious fellowships for researchers. | UK, Funding | 181 | | [**Marie Skłodowska-Curie Actions (MSCA)**](https://lnkd.in/g3_68yNG) | EU fellowships for researchers. | UK, EU, Funding | 182 | | [**Wellcome Trust Fellowships**](https://lnkd.in/gcSt5p8C) | Postdoc funding in biomedical research. | UK, Biomedical | 183 | | [**Oxford Postdoc**](https://lnkd.in/gfUFMvV8) | Postdoctoral resources at Oxford University. | UK, Academia | 184 | | [**Cambridge Postdoc**](https://lnkd.in/gcHnUZPJ) | Cambridge University postdoctoral programs. | UK, Academia | 185 | | [**EMBO**](https://lnkd.in/g6J43XVP) | European Molecular Biology Organization fellowships. | Europe, Life Sciences | 186 | | [**Max Planck Society**](https://lnkd.in/gkXRjAXm) | Research institutes offering postdoc positions. | Germany, Research | 187 | | [**Helmholtz Association**](https://lnkd.in/gkNcYj-s) | German research network with postdoc opportunities. | Germany, Research | 188 | | [**CERN Fellowship**](https://lnkd.in/gZGRX352) | Prestigious research fellowships in physics. | Europe, Physics | 189 | | [**EPFL Postdoc**](https://lnkd.in/ggNYHWAH) | Postdoctoral programs at EPFL Switzerland. | Switzerland, Academia | 190 | | [**Leibniz Association**](https://lnkd.in/g3PiFmrv) | Research institutes across Germany. | Germany, Research | 191 | | [**ETH Zurich Postdoc**](https://lnkd.in/g5vxa4VV) | ETH Zurich postdoctoral fellowships. | Switzerland, Academia | 192 | | [**Australian Research Council (ARC)**](https://lnkd.in/gMRpReZY) | Funding body for Australian research. | Australia, Funding | 193 | | [**University of Sydney Postdoc**](https://lnkd.in/gjmu5SR9) | Postdoctoral programs at Sydney University. | Australia, Academia | 194 | | [**University of Melbourne Postdoc**](https://lnkd.in/g2B3J4Yg) | Melbourne University postdoc programs. | Australia, Academia | 195 | | [**University of Queensland Postdoc**](https://lnkd.in/gxQi4VPc) | UQ postdoctoral opportunities. | Australia, Academia | 196 | | [**University of Auckland Postdoc**](https://lnkd.in/gHrdXFHJ) | New Zealand postdoctoral programs. | NZ, Academia | 197 | | [**JSPS Fellowships**](https://lnkd.in/ggk4CBzZ) | Prestigious postdoctoral fellowships in Japan. | Japan, Funding | 198 | | [**NUS Postdoc**](https://lnkd.in/gVsf4SpD) | National University of Singapore opportunities. | Singapore, Academia | 199 | | [**CAS Postdoc**](https://lnkd.in/gPNN7j_P) | Chinese Academy of Sciences postdoctoral programs. | China, Research | 200 | | [**IISc Postdoc**](https://lnkd.in/gtx8eHfp) | Postdoc opportunities at Indian Institute of Science. | India, Academia | 201 | | [**QNRF**](https://lnkd.in/gHjgyPvd) | Qatar National Research Fund fellowships. | Qatar, Funding | 202 | | [**KAUST Postdoc**](https://lnkd.in/gYc2kmz5) | King Abdullah University of Science and Technology postdoctoral programs. | Saudi Arabia, Academia | 203 | | [**Talent**](https://au.talent.com/jobs?k=Postdoctoral+Fellow&l=Australia&date=3&id=8fdb8505dce2) | Find postdoc and other job austrial | austrial | 204 | | [**metacareers**](https://www.metacareers.com/jobs?q=Postdoc) | Qatar National Research Fund fellowships. | International Funding | 205 | | [**scholarshipdb.net**](https://scholarshipdb.net/scholarships-in-Germany?q=Postdoctoral%20Computer%20Science&listed=Last%207%20days) | Qatar National Research Fund fellowships. | International Funding | 206 |
207 | 208 |
209 |

🔍 Job Searching Company

210 | 211 | | Title/Link | Description | Tags |Country|Feedback| 212 | |---|---|---|---|---| 213 | | [**Codeaza**](https://codeaza.notion.site/1fc1e0fd9cc581d0aa3ce6931996a157) | Largest professional job board. | Jobs, AI,others |Pakistan| 214 | | [**United Arab Emirates University**](https://jobs.uaeu.ac.ae/) | Find job in UAE University. | Jobs, AI,others |United Arab Emirates | 215 | | [**AI Data House SMC (pvt) Limited**](https://jobs.uaeu.ac.ae/) | Find job in UAE University. | Islamabad |--- | 216 | 217 |
218 | 219 |
220 |

Postdoc searching in Country

 221 | 222 | ## 📚-**UK** 223 | 224 | | # | University / Lab | Professor Name | Designation | Domain | Applied Date | Job Link | Email | Job Title | Follow-up Date | Mushtaq | Note | 225 | |----|----------------------------------|------------------------|---------------------|--------|--------------|----------|-------|----------------------|----------------|---------|-------| 226 | | 3 | The Artificial Intelligence Lab | ANN NOWÉ | Head | AI | | | [Email](mailto:ann.nowe@vub.be) | | | ✅ | | 227 | | 4 | The Artificial Intelligence Lab | BART DE BOER | Professor | AI | | | [Email](mailto:bart.de.boer@ai.vub.ac.be) | | | ✅ | | 228 | | 5 | The Artificial Intelligence Lab | BART BOGAERTS | Professor | AI | | | [Email](mailto:bart.bogaerts@vub.be) | | | ✅ | | 229 | | 6 | The Artificial Intelligence Lab | WIM VRANKEN | Professor | AI | | | [Email](mailto:wim.vranken@vub.be) | | | ❌ | | 230 | | 7 | The Artificial Intelligence Lab | TOM LENAERTS | Professor | AI | | | [Email](mailto:Tom.Lenaerts@vub.be) | | | ❌ | | 231 | | 8 | The Artificial Intelligence Lab | PIETER LIBIN | Professor | AI | | | [Email](mailto:pieter.libin@vub.be) | | | ❌ | | 232 | | 9 | The Artificial Intelligence Lab | PAUL VAN EECKE | Professor | AI | | | [Email](mailto:paul@ai.vub.ac.be) | | | ❌ | | 233 | | 10 | The Artificial Intelligence Lab | LYNN HOUTHUYS | Professor | AI | | | [Email](mailto:lynn.houthuys@vub.be) | | | ❌ | | 234 | | 11 | The Artificial Intelligence Lab | GERAINT WIGGINS | Professor | AI | | | [Email](mailto:geraint@ai.vub.ac.be) | | | ❌ | | 235 | | 12 | The Artificial Intelligence Lab | JOHAN LOECKX | Professor | AI | | | [Email](mailto:jloeckx@ai.vub.ac.be) | | | ❌ | | 236 | | 13 | University of Cambridge | Georgie Willsher | Professor | AI | | | [Email](mailto:georgie.willsher@mrc-cbu.cam.ac.uk) | | | ✅ | | 237 | | 14 | University of Cambridge | Dr Nicholas Walton | Professor | AI | | | [Email](mailto:naw@ast.cam.ac.uk) | | | ✅ | | 238 | | 15 | Aalto University | Samuel Kaski | Professor | AI | | | | | | ❌ | | 239 | | 16 | Aalto University | Fang Wang | Professor | AI | | | [Email](mailto:fang.wang@aalto.fi) | Postdoc | | ✅ | reply | 240 | | 17 | Aalto University | Prof. Siavash Khajavi | Professor | AI | | | [Email](mailto:siavash.khajavi@aalto.fi) | PhD | | ❌ | | 241 | | 18 | University of Derby | Farid Meziane | Professor | AI | | | [Email](mailto:f.meziane@derby.ac.uk) | Research assistant | | ✅ | | 242 | | 19 | University of Virginia | Negin Alemazkoor | Assistant Professor | AI | | | [Email](mailto:na7fp@virginia.edu) | PhD Postdoc | | ✅ | | 243 | 244 | ## 📚-**CHINA** 245 | 246 | | # | University / Lab | Professor Name | Designation | Domain | Applied Date | Job Link | Email | Job Title | Follow-up Date | Mushtaq | Note | 247 | |----|----------------------------------|------------------------|---------------------|--------|--------------|----------|-------|----------------------|----------------|---------|-------| 248 | | 1 | Shenzhen University | ANN NOWÉ | Head | AI | | [Link](https://ias.szu.edu.cn/en/index.htm) | [Email](mailto:ann.nowe@vub.be) | | | ✅ | | 249 | 250 |
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