10 algorithm-"Multiple"-"Prof"-"Simons-Foundation" research jobs at King's College London
Sort by
Refine Your Search
-
Listed
-
Program
-
Field
-
deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with experience in: Deep learning Medical imaging computing
-
6. Desirable criteria Evidence of active collaboration with dry lab and co-development of algorithm for the prediction of epitopes. Downloading a copy of our Job Description Full details of the role
-
learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial analysis Strong publication record Experience in women and children’s
-
-disciplinary research environment Desirable criteria 1. Experience in devising and developing novel machine learning algorithms 2. Hands on experience with ROS and physical robots 3. Excellent
-
skills, including the ability to summarise and present research findings. Excellent organisational and time management skills, with the ability to manage multiple tasks and deadlines, and operate within
-
Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of Electronic Health Records from people with epilepsy across multiple NHS
-
generative Large Language Models (LLMs), to interrogate a very large sample of Electronic Health Records from people with epilepsy across multiple NHS hospitals. They are expected to have some experience
-
, the development and fine-tuning of vision foundation models, multiple instance learning, survival analysis, and interpretable model development. You will also lead efforts in building multimodal deep learning
-
multiple fields and audiences. This is a full time post (35 Hours per week), and you will be offered an a fixed term contract until 31 August 2028. About you Please note that these are development positions
-
advantage. The PDRA will expand their research skills through working with multiple sources across the study region and its zones of contact as feasible. They should have knowledge of the relevant history and