Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Postdoctoral Researcher in Multimodal Machine Learning for Precision Cancer Medicine The Machine Learning in Biomedicine (MLBioMed) research group at the Institute for Molecular Medicine Finland
-
The Machine Learning for Health team in the Data Science and Genetic Epidemiology Lab at the Institute for Molecular Medicine Finland (FIMM) , University of Helsinki, is currently seeking a highly
-
within a Research Infrastructure? No Offer Description The Machine Learning for Health team in the Data Science and Genetic Epidemiology Lab at the Institute for Molecular Medicine Finland (FIMM
-
learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen
-
Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted
-
biogeography, with a strong emphasis on computational data analysis. Alternatively, the candidate may hold a master’s degree in statistics, machine learning or related field, accompanied by prior experience
-
, machine learning, time-series analysis, causal inference) Previous work experience with sensitive personal data Previous experience in pharmaceutical, medical technology, or hospital domain Experience with
-
, normalization, dimensionality reduction) to downstream interpretation (differential expression, gene set enrichment, and cell type annotation). Implement Machine Learning Approaches, including deep learning
-
to teaching or supervision duties. Requirements The successful applicant should have a doctoral degree in statistics, mathematics, machine learning, or other relevant field, and experience in developing and
-
, calibration, and the development of analysis tools and software. Our key focus areas are the physics of jets, top quarks, and EWSB, including the development of novel machine-learning methods for high-energy