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AI & Machine Learning (biological physics) PhD Projects, Programs & Scholarships

We have 6 AI & Machine Learning (biological physics) PhD Projects, Programs & Scholarships

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  Designing nanomachines using computer simulations and artificial intelligence
  Dr A Saric
Applications accepted all year round

Funding Type

PhD Type

We are looking for highly motivated students to join our interdisciplinary team for an exciting project at the interface of physics, computer science, and biology.
  Computer aided design methods for mammalian genome engineering
  Research Group: Institute of Quantitative Biology, Biochemistry and Biotechnology
  Dr G Stracquadanio, Dr F Menolascina
Application Deadline: 5 January 2020

Funding Type

PhD Type

Computer Aided Design (CAD) software assists engineers in designing systems in-silico, thus reducing the time-to-market of a product and increasing the overall productivity.
  Data-driven design of complex functional proteins
  Research Group: Institute of Quantitative Biology, Biochemistry and Biotechnology
  Dr G Stracquadanio
Application Deadline: 5 January 2020

Funding Type

PhD Type

Second Supervisor. Chris Wells Wood, School of Biological Sciences, [email protected] Proteins are the building blocks of life as they are responsible for all the functions in a cell.
  Machine learning methods to identify regulatory networks controlling proliferation and senescence
  Research Group: Institute of Quantitative Biology, Biochemistry and Biotechnology
  Dr G Stracquadanio, Dr T Ly
Application Deadline: 5 January 2020

Funding Type

PhD Type

Cells are controlled by an intricate network of interactions between genes and proteins, which mediate fundamental processes, such as metabolism, growth and proliferation.
  Accurate methods for rare variant calling in low-coverage cancer exome sequencing data
  Research Group: Institute of Quantitative Biology, Biochemistry and Biotechnology
  Dr G Stracquadanio, Dr G Sanguinetti
Application Deadline: 5 January 2020

Funding Type

PhD Type

High-throughput sequencing technologies enabled the characterization of the genomic landscape of many tumours (1,2).
  Learning from minimal eukaryotic genomes; using machine learning to understand the rules of life
  Research Group: Institute of Quantitative Biology, Biochemistry and Biotechnology
  Dr G Stracquadanio
Application Deadline: 5 January 2020

Funding Type

PhD Type

The genome of every organism encodes genes and regulatory elements required for building and maintaining cells. However, it is still unknown what is the minimal set of genomic elements necessary and sufficient for cellular life.
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