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​Ph.D. studentships


Ph.D. student positions are currently available in our group.

Our group works on challenging problems on the boundary between artificial intelligence, biology and biomedicine. We are looking for Ph.D. students to join our team for developing novel computational algorithms to integrate, mine and analyze biological data. The focus of the position will be on novel methods in artificial intelligence to address problems in making sense of biological data. Areas of application include biomedicine (rare and common diseases), pharmacology (drug repurposing, drug target discovery), and biodiversity (understanding environments and ecosystems).

Successful candidates should hold a M.S. or B.S. in computer science, mathematics, statistics, or bioinformatics. Candidates should have excellent programming skills and experience.  A strong background in algorithms, data mining, machine learning, or knowledge representation is desirable.

Ph.D. students at KAUST are entitled to a full scholarship, including free housing, health care, and a monthly stipend (details).

Candidates who are willing to work in an exciting environment on computational biology, biomedical informatics, systems biology, and knowledge representation are encouraged to apply. To apply, please send a CV and a brief description of previous research to Robert Hoehndorf. Applicants are required to enroll as Ph.D. students at KAUST, and the final recruitment decision is made by KAUST. All requirements for enrollment are available here.

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Postdoctoral researcher


We are seeking a post-doctoral researcher with research interests in applying machine learning in computational biology and biomedicine.
 

Job description:

The postdoctoral researcher will work on developing methods to analyze graph-structed data in biology and biomedicine. The main areas of application are understanding the molecular basis of disease and phenotypes, and the environmental influences that influence the development of the phenotype. The ideal candidate will have a background in machine learning or datamining and an interest in developing and improving methods to analyze data represented as graphs or characterized with biomedical ontologies. 
The postdoctoral researcher will be responsible for carrying out research independently and collaboratively with other members of the research group, and present research results at conferences and in scientific journals.
 

Benefits:

Postdoctoral researchers are entitled to a competitive salary (commensurate with the applicant's qualifications), free, fully-furnished housing on the KAUST campus, free medical and life insurance, free education at KAUST schools for the postdoctoral researchers' children, and relocation allowance.
 
KAUST will be responsible for the actual recruiting decision, appointment offers and employment benefits.

​​​How to apply:

To apply, please send a CV and a brief description of previous research to Robert Hoehndorf.​

Research internships and visiting students


We currently have multiple research internships available. 

Our group works on challenging problems on the boundary between artificial intelligence, biology and biomedicine. We are looking for visiting research students to join our team for a period of three to six months. The aim of the internship will be to work on computational algorithms to integrate, mine and analyze biological data and gain research experience.

Candidates should be in their final year of a B.S. degree, or hold a B.S. degree, in computer science, mathematics, statistics, or bioinformatics. Candidates should have strong programming skills.  A background in algorithms, data mining, machine learning, or knowledge representation is desirable.

Visiting research students are entitled to a monthly stipend, free accommodation, and round-trip airfare.

Candidates who are willing to work in an exciting environment on computational biology, biomedical informatics, systems biology, and knowledge representation are encouraged to apply. To apply, please send a CV and a brief description of previous research to Robert Hoehndorf.
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