Doctoral student in Mathematical Statistics with focus on proteomic modelling Full-time NEW

Doctoral Student in Mathematical Statistics with Focus on Proteomic Modelling

Lund University was founded in 1666 and is repeatedly ranked among the world's top universities. The University has around 46,000 students and 8,500 staff based in Lund, Helsingborg, and Malmö. We are united in our efforts to understand, explain, and improve our world and the human condition.

Description of the Workplace

The workplace is the division of Mathematical Statistics with approximately 25 teachers, researchers, and doctoral students. Research areas within mathematical statistics are probability theory and statistical theory. The main task of probability theory is to develop mathematical models for the description and analysis of random processes, and to study the mathematical properties of such models. Within the statistical theory, principles and methods are studied to build and test the models with the help of empirical facts and data. Applications are found in all areas of society with an emphasis on science, technology, medicine, and economics.

Being a Doctoral Student

As a doctoral student, you are both admitted as a student and employed at Lund University. As a doctoral student, you will be trained in a scientific approach. In short, you will be trained to think critically and analytically, to solve problems independently using the right methods, and to develop an awareness of research ethics. In addition, you will have the opportunity to work on projects, to develop your leadership and pedagogical skills. Throughout your studies, you will be guided by supervisors. Doctoral studies end with a thesis and a doctoral degree.

More about being a doctoral student at LTH on lth.se.

Subject and Project Description

An important aspect of enabling personalized medicine is to map the high-dimensional protein interactions that govern our health. This project aims to model which types of protein interactions drive the progression of ARDS in intensive care patients with sepsis. To enable this, we will develop information-theoretic machine learning methods to determine which protein interactions are driving disease progression and relate these to relevant biological structures.

The project is funded by ELLIIT, one of Sweden's strategic research initiatives, and is part of a collaboration with researchers at BTH and Region Skåne.

Work Duties

You will primarily devote yourself to your doctoral education, which includes participation in research projects as well as doctoral courses, seminars, and conferences. The research deals with theory and method development in information theoretical machine learning focused on proteomics data, sparse structures, and convex optimization.

The duties also include participation in teaching and other departmental work at Mathematical Statistics, amounting to no more than 20% of working time.

Qualifications

To be eligible for admission and employment as a doctoral student, you must fulfill the requirements below.

Admission Requirements

A person meets the general admission requirements for third-cycle courses and study programs if the applicant:

• has been awarded a second-cycle qualification, or
• has satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second cycle, or
• has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

A person meets the specific admission requirements for third cycle studies in Mathematical Statistics if the applicant has:

• at least 90 credits of relevance to the subject area, of which at least 45 credits are at second-cycle level.

Additional Requirements

In order to complete the doctoral programme in question, the following are also required:

• at least one course in programming, one in optimization, and one in machine learning
• at least one advanced-level course in stochastic processes, or in related subjects such as time series analysis, spatial statistics, spectral analysis, or statistical learning
• good ability to work independently and to formulate and tackle research problems.
• good written and oral communication skills
• good ability to cooperate
• very good knowledge of English, spoken and written

Other Qualifications

For the doctoral programme in question, the following are considered as other qualifications:

• ability (e.g., demonstrated in a thesis project) to develop, implement, and apply relevant statistical methods to data and to critically evaluate the results
• experience in signal processing, machine learning, optimization, and time series analysis
• programming experience (preferably in Python or MATLAB)

We Offer

Lund University is a public authority which means that employees get particular benefits, generous annual leave, and an advantageous occupational pension scheme. It is advisable to enter information on what your unit specifically can offer as a workplace.

More about working at Lund University on lu.se.

About the Employment

The employment is a fixed-term employment at full time, starting as agreed. Third cycle studies at LTH consist of full-time studies for 4 years. In the case of teaching and other departmental duties, the employment is extended accordingly. Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 §§.

More about terms of employment for doctoral students on Lund University's Staffpages. https://www.staff.lu.se/research-and-education/research-support/doctoral-education/terms-employment-doctoral-students

Other

Any other information such as where and when interviews will be held.

How to Apply

Applications shall be written in English and include:

• CV and a cover letter stating the reasons why you are interested in the doctoral programme/employment and in what way the research project corresponds to your interests and educational background.
• Copies of issued study certificates and/or awarded degree certificates. These must confirm that you meet the general and specific admission requirements for the doctoral programme and show that you have the subject knowledge required for the doctoral programme project.
• Other documents you wish to be considered (grade transcripts, contact information for your references, letters of recommendation, etc.)

We welcome your application.