Doctoral Position in Theoretical Physics
Lund University Department of Astronomy and Theoretical Physics
Sweden

Doctoral Student in Theoretical Physics with Focus on Computational Biology

Lund University, Faculty of Science, Department of Astronomy and Theoretical Physics

Lund University was founded in 1666 and is repeatedly ranked among the world’s top 100 universities. The University has around 44 000 students and more than 8 000 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition.

The Faculty of Science conducts research and education within Biology, Astronomy, Physics, Geosciences, Chemistry, Mathematics and Environmental Sciences. The Faculty is organized into nine departments, gathered in the northern campus area. The Faculty has approximately 1500 students, 330 PhD students and 700 employees.

The activities of the Department of Astronomy and Theoretical Physics are centred around three main areas: Astronomy and Astrophysics; Theoretical Particle Physics; and Computational Biology and Biological Physics. Our research is driven by our curiosity to understand reality, from the smallest of particles to the largest astronomical structures, as well as the structure of Life itself. We have a strong international position in all three areas, and we are actively working on further strengthening our international profile.

We announce a position as doctoral student in theoretical physics with focus on computational biology at the unit for Computational Biology and Biological Physics (www.atp.lu.se/cbbp). Research involves development of machine learning algorithms with focus on automation of design choices in artificial neural networks for the analysis of medical and biological data.

Description of work

Recent developments in machine learning has led to renewed interest in artificial neural networks. Fast hardware and new efficient network designs have made it possible to create very complex and high performing networks, a discipline that has come to be called "deep learning". The advent of new network options introduces additional settings that are often optimized by manual inspection or simple empirical strategies in different applications.

The PhD student will be part of the group for Computational Biology and Biological Physics at the Department of Astronomy and Theoretical Physics. The PhD student will develop algorithms for automation of design choices for artificial neural networks, and apply them to the analysis of medical and biological data in established cross-disciplinary collaborations with medical and other science research groups.

The main task for a doctoral student is the postgraduate studies, which includes both participation in research projects and postgraduate courses. The work may also include participation in teaching and other departmental work, however, at a maximum of 20%.

Qualifications

The position is open to students of all nationalities who fulfil the basic and special eligibility demands in the study plan http://www.science.lu.se/sites/science.lu.se.internal/files/syllabi_theoretical_physics.pdf . In brief, the requirements are that the student, at the time of starting the PhD studies, have completed a bachelor degree in biomedical subjects, chemistry, physics, mathematics or computer science and 60 second-cycle credits in computational biology, computer science or associated subjects, i.e. a total of at least four years of full-time University studies (240 ECTS credits).

Basis of Assessment

Regulations concerning appointment as a full PhD student can be found in HF 5 Chap 1-7§§ and SFS 1998:80. Those who hold a doctoral student appointment must first be accepted for postgraduate study. To be accepted, a student must be judged to have the competence necessary to complete a PhD during the tenure of the appointment. Among candidates, a ranking will be based on grades, the quality of undergraduate theses, if any, letters of recommendation, other relevant information provided, and ultimately interviews.

Keen interest in machine learning, specifically artificial neural networks, as well as algorithm development and good programming skills are a prerequisite. Previous experience artificial neural networks and how to use them in applications is an advantage. High level of both written and spoken English is necessary.

In addition to pursuing postgraduate studies, the doctoral student may be required to perform other duties - including research, teaching and administration - according to the specific regulations.

Application procedure

Applications should include a curriculum vitae, a description of research interests and past experience, copies of degrees, diplomas and grades, and copies of any previous research-related work. The CV should contain at least date and place of birth, nationality, address, education, and language skills, but may also contain e.g. additional skills, personal interests, honors and awards, teaching experience, conference and summer school participation, and publication lists. Upon request the applicants must be able to show original documents of degrees etc.

The application should also include the names, positions, telephone numbers and e-mail addresses of at least two persons who have agreed to serve as a reference for the applicant. Note that reference letters should not be sent in connection with the application; we will contact the reference persons when required.

Type of employment

Limit of tenure, four years according to HF 5 kap 7§.

Lund University welcomes applicants with diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset. We kindly decline all sales and marketing contacts.

To apply, please click the button "Login and apply"

Type of employment Temporary position longer than 6 months
First day of employment 2021 by agreement
Salary Monthly salary
Number of positions 1
Working hours 100
City Lund
County Skåne län
Country Sweden
Reference number PA2021/1733
Contact
Mattias Ohlsson, Professor, +46-46-2227782, mattias.ohlsson@thep.lu.se
Union representative
OFR/ST:Fackförbundet ST:s kansli, 046-222 93 62
SACO:Saco-s-rådet vid Lunds universitet, 046-222 93 64
Published 14.Jun.2021
Last application date 31.Jul.2021 11:59 PM CET


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