Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
Description Primary Duties & Responsibilities: Implements: Algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical]; Data management
-
Sciences o Cryptography o Data Sciences, Complex Networks, Mathematical Biology o Quantum Computation & Information Science o Post-Quantum Cryptography, Homomorphic Encryption and Computing o Theoretical and
-
Ability to do original and outstanding research in computational biology, and expertise in computational methods, data analysis, software and algorithm development, modeling machine learning, and scientific
-
motivated to move the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our team, you get the opportunity to use the latest algorithms in machine learning
-
the Generative Flow Network (GFlowNet) algorithm. We plan to further enhance this algorithm to consider the shape of biological binding sites and incorporate modules for optimizing physicochemical properties
-
the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our group, you get the opportunity to use the latest algorithms in machine learning for improving
-
review. You will possess master level quality, accuracy, speed and knowledge for your assigned specialist area and execute reflex testing algorithms. Work schedule: 100% FTE, Fixed Duration Appointment
-
of biological databases, algorithms and pipelines; experience in working with phylogenetic/ genomic/transcriptomic/systems biology tools will be a bonus; being well organized, eager to learn and ready to
-
Requirements . Please note: You can apply online. We will … Where to apply Website https://www.academictransfer.com/en/jobs/355665/phd-position-algorithms-for-mic… Requirements Additional Information Website
-
or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning