Sort by
Refine Your Search
-
health data, such as electronic health records or biobank-scale resources (e.g., UK Biobank, All-of-Us, FinnGen). Familiarity with machine learning approaches, such as penalised regression, deep learning
-
Engineering, etc.), expertise in cutting-edge AI and machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role
-
changes and established markers for Alzheimer's disease. The project may also include machine learning methods to estimate individuals' biological age. The project is based on existing data from a prominent
-
range of teaching and life learning programmes which address the needs of students and professional groups who are interested in and undertaking work relevant to child health. GOS ICH holds an Athena SWAN
-
expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
-
, single-cell analysis, and machine/deep learning (preferred but not required). Strong programming and statistical skills (e.g., Python, Perl, R, Bash). Track record of first-author research papers. Strong
-
demonstrates excellent scientific, interpersonal, and communication skills. Technical proficiency, scientific creativity, collaboration with others and independent thought. To learn more and apply, please visit
-
independent thinkers, curious and intrinsically motivated, with a passion for basic research. Postdoctoral fellows in the lab bring or learn diverse tools, including: Protein expression and purification
-
to work within a team environment. Adaptability to a fast-paced, dynamic environment. Multitasking essential. To learn more and apply, please visit: https://careers.dana-farber.org
-
focus of the position is on application and advancement of modern artificial intelligence (AI) methods in drug discovery and development. An ideal candidate will have strong background in machine learning