Ben Carter’s Post

In life sciences, there's a huge amount of complex data being produced, especially with new technologies in genomics, proteomics, and other fields where researchers are generating vast amounts of data at an unprecedented rate. This mass of data can be overwhelming to analyse and interpret though. That's where AI comes in, changing and enhancing how we manage and interpret all this information. AI algorithms, particularly those involving machine learning and deep learning, are adept at processing large datasets quickly and accurately. Life Sciences, as an industry, is seeing a huge shift towards hiring talent that has a background in AI and data science to facilitate the necessary business changes that such huge amounts of data dictates. #LifeSciences #AIinScience #MachineLearning #DeepLearning #DrugDiscovery #Staffing #Recruitment

In this market, finding good AI talent is hard. Luckily BAIR is here to make your life easier! They've helpfully compiled a list of our newest graduates that are entering the market. If you're hiring, please give them a look!

2 of 10 #job #openings on 1/16 ✴️ Zoom ✴️ #research #scientist #ai #llm #checkitout and #share #opportunity #usa #usjobs #hiring #hiringnow #open #job #reshare #engineeringjobs #engineering #givingback #spreadtheword #connectandgrow #repost

Opportunity

Esteemed Colleagues and Research Community, I am currently examining potential collaborations at the thrilling juncture of Machine Learning (ML) and medical research. The abundance of data in this field provides an ideal setting for the application of various ML and Deep Learning techniques. I am particularly keen on predictive modeling for risk assessment, time series analysis of patient health progress, identifying patient subgroups through clustering, and uncovering hidden patterns using advanced neural networks (NNs). This research has the potential to significantly influence patient care. I am in search of partners who are enthusiastic about harnessing data science to enhance healthcare outcomes. Whether your expertise is in machine learning, medical research, data analysis, or a related field, I believe your contribution could be invaluable. Our collaboration could involve data analysis, model development, co-authoring research papers, or even joint applications for funding. If you are interested in participating in this pioneering research, please do not hesitate to connect with me. I am eager to discuss how we can collaborate to advance this critical field.

As artificial intelligence (AI) transforms sectors like healthcare, climate science and education, research is essential to unlock its full potential, refine algorithms, and ensure responsible deployment. Explore 120 AI jobs across Europe, encompassing PhD, postdoc, researcher, professor and tenure-track positions. Apply today! Curious about how universities are shaping the future with AI research? Explore our articles: 'Advancing Sport and Health Sciences with Artificial Intelligence' (https://bit.ly/3G0TH9L) with University of Jyväskylä and 'Your Path to an Excellent Career in AI at Radboud University' (https://bit.ly/49zyOjJ). Dive into these brief yet insightful reads and stay on the cutting edge of AI research.

Job opportunity! Lead Researcher in Network System Design for AI, 170-250k CHF + bonus scheme - #Switzerland. To find out more, please visit the link below #AI #Software #DataScience #ImageProcessing #Robotics #ComputerVision #3DReconstruction #ICResources #TechnologyRecruitment #NLP #DeepLearning #TechnologyCareers #MachineLearning #TechnologyJobs #ArtificialIntelligence #Graphics #DataEngineering

As the demand for AI talent rises, tech companies are enticing top researchers with generous salary packages. This has led to widespread collaboration between private companies and universities, where industry funds research and provides necessary resources. Nonetheless, there are concerns about how corporate interests may sway research priorities. #ai #aitalent #academics #universities #aiethics

I have received the recording of my presentation at the "AI and Libraries" conference on the "Impact of AI on Academic Library Research Support Services and Literature Review Process". Next month, I will present about using AI for a systematic review and, hopefully, share my teaching and consulting experience on reviewing research using AI at another conference. I am in the middle of a professional pivot where I am exploring new opportunities at the intersection of research synthesis, emerging AI technology, teaching, and research.

I am thrilled to announce that I have completed my masters in Data Science and I am excited to share a milestone achievement in my academic journey! My thesis, "Comparison of Machine Learning models for Detection of Ineffective Efforts during Expiration (IEE)," conducted in collaboration with Uniklinik Aachen, focuses on newborn care. This research is not just a step forward in my career but also a contribution to neonatal healthcare, leveraging the power of explainable AI (xAI). 🔍 Key Highlights: - Developed an approach using machine learning to detect IEE in newborns, a critical aspect often overlooked due to the subtlety of signs. - Utilized tabular data from ventilator machines to generate images, preparing two distinct data types: line curve and area under curve. - Implemented two types of Convolutional Neural Networks (CNNs): traditional and channel attention-based, comparing their efficacy in image classification tasks. This comparison was instrumental in understanding the nuances of each model in handling complex medical imagery. - Employed interpretation techniques, Grad-CAM and LIME, to make the models' decision-making processes transparent and understandable. This is crucial for medical professionals relying on our models for diagnostic assistance. -Used PyTorch libraries to develop the study. This work is just the beginning of my quest to bridge the gap between advanced machine-learning techniques and practical medical applications. I am eager to explore further collaborations, share knowledge, and contribute to transformative healthcare solutions. A heartfelt thank you to my mentor Camelia Oprea, peers, and the Uniklinik Aachen team for their support and collaboration. Let's continue to push the boundaries of what's possible in medical AI! #MachineLearning #AIInHealthcare #PyTorch #MedicalImaging #xAI #ThesisHighlight #NewbornCare #opentowork