56 phd-computer-science-"IMPRS-ML"-"IMPRS-ML" Postdoctoral positions at Nature Careers in Germany
Sort by
Refine Your Search
-
PhD or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated experience in computational and statistical
-
computer science, bioinformatics or related fields Solid understanding of machine and deep learning and relevant frameworks (e.g. Pytorch or Tensorflow, Keras, scikit-learn, OpenCV) Proficiency in Python, Linux and
-
. Your profile: PhD in synthetic biology, natural product chemistry, microbiology, life science, or a related discipline Extensive experience in synthetic biology and molecular biology techniques
-
physical sciences, engineering, advanced microscopy techniques, and DNA nanotechnology. Biochemists, synthetic biologists, bioengineers, chemical biologists, chemists, or candidates with a computational
-
in the field of microbial ecology, molecular biology, microbiology or comparable with and a very good PhD Professional experience as a postdoc and experience in the supervision of qualification theses
-
of improved genetically encoded hypoxia reporters REQUIREMENTS: The candidate must hold a PhD in life sciences (obtained no longer than 3 years ago) A strong interest and experience in confocal and / or
-
of reports, publication of results Your qualifications: Completed university studies (M.Sc./ M.Eng.) and PhD in polymer science or a comparable field of study Relevant experience in thermal analysis and fire
-
, epigenetics, cardiovascular science, computational biology, or a related field A strong background in chromatin biology, gene regulation, and/ or cardiovascular biology Prior experience with genomics
-
techniques to challenging biological systems. Requirements: Ideal candidates should hold an excellent PhD in structural biology, biochemistry, biophysics or in an equivalent area. Experience in membrane
-
prostate tumor samples. This position requires a strong background in both experimental proteomics and computational data science (R and Python), with an emphasis on LC-MS/MS workflows and long-term cohort