Faculty Cluster Hire in Data Analytics for Biological and Physical Sciences

The Hong Kong University of Science and Technology
Hong Kong (HK)
October 05 2018
Other, Education
Organization Type

Hong Kong University of Science and Technology

School of Science

Faculty Cluster Hire in Data Analytics for Biological and Physical Sciences

The Hong Kong University of Science and Technology (HKUST) has embarked on a cluster hiring plan to recruit multiple faculty members in data analytics for biological and physical sciences in the School of Science.    

As a rapidly increasing trend, research at the forefront in biological and physical sciences produces huge datasets.  The success of these research projects thus highly relies on the development of data analytics tools to rapidly and accurately extract useful information from large datasets.  As primary beneficiaries of new concepts in data science, the applications in biological and physical sciences reciprocally push the boundaries of data science.  Therefore, the development in the area of data analytics can greatly benefit multiple disciplines at HKUST including Mathematics, Chemistry, Life Science, Physics and Ocean Science.  To this end, we are looking for top-tier researchers who are capable of assembling different knowledge domains and become the agents that can, and will, facilitate cross-disciplinary collaborations among all departments, bringing biological, physical and mathematical sciences together and closer to industry.

Applications are invited for substantiation-track faculty positions at all ranks of Professor/Associate Professor/Assistant Professor in the School of Science, HKUST. Joint appointments in more than one academic department are encouraged especially for senior hires.  Successful candidates should have a PhD degree in related fields and preferably have a demonstrated solid research track record in data analytics.  They are expected to develop cutting- edge data analytics tools for biological and physical sciences.  The areas of focus include biological structures and dynamics, data-driven quantum chemistry tools, information physics, genome-mining based bioactive compound discovery, molecular and interaction networks, ocean data mining and visualizations, digital pathology and genome analysis, health informatics, cheminformatics, bioinformatics, and image analysis.  Apart from an exceptional research record, successful candidates should demonstrate strong potential in teaching.

Salary is highly competitive and will be commensurate with qualifications and experience.  Fringe benefits include medical and dental benefits, annual leave and housing benefits where applicable.  For appointment at the rank of Assistant Professor, initial appointment will normally be on a three-year contract.  A gratuity will be payable upon completion of contract.

Founded in 1991, HKUST is a world-renowned international research university in Asia’s most vibrant city.  The University comprises more than 500 faculty members whose research ranges from science to engineering, business, humanities and social sciences.  It promotes interdisciplinary studies, dedicated to educating all-rounded students to acquire a strong entrepreneurial spirit and innovative thinking with a global outlook.  HKUST has been ranked among the top universities in Asia by QS Asian University Rankings.  It ranks 16th in the latest Global Employability University Survey reported by the New York Times, and remains No. 1 in Greater China area.  English is the medium of teaching, research and administration.

Application Procedure
Applications should include a full CV, statement of research, statement of teaching, and arrange three letters of recommendations to the Search Committee for Cluster Hire in Data Analytics for Biological and Physical Sciences, by e-mail to hiring.data.analytics@ust.hk.

Candidates are strongly encouraged to submit their applications by 1 December 2018, and review of applications will begin immediately afterwards.  However, applications will be accepted until the positions are filled.


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