PhD Position in Machine Learning in Medicine University of Copenhagen Department of Computer Science

PhD fellowships in Machine Learning in Medicine PhD Project in computer-aided abdominal procedures Department of Computer Science Faculty of SCIENCE University of Copenhagen Department of Computer Science (DIKU) invites applicants for PhD fellowships in Machine Learning in Medicine. This project is a part of a collaboration between DIKU and Rigshospitalet, and financed by Novo Nordisk Foundation. Start date is (expected to be) 1 January 2023 or as soon as possible thereafter. The project Funding for two Ph.D. positions working on two projects is available. One of the positions is the development of shape-based machine learning with the application on pancreatic image analysis and surgery planning. The second position is on the development of diagnostic machine learning solutions for classification and prognostication of gastrointestinal diseases. The Ph.D. fellows will work on both theoretical aspects of machine learning solutions development and the application of the developed solutions on clinically relevant problems. a) Understanding the object shape from an image is an advanced human-level task. Despite extraordinary success of ML solutions in medical image analysis, the inclusion of shape with ML for medical image analysis remains under investigated. However, the shape has been historically playing a critical role in computerized diagnosis. Human organs follow predetermined anatomical patterns, justified by organ functionality and positioning in the human body. Moreover, many diseases do not affect organ shapes, or alter organ shapes in a very predictable manner. Shape models are also used for computer-aided diagnosis, organ morphometry, pathology detection, and inter-organ spatial relationships quantification. Finally, anatomical knowledge is the foundation of physicians’ decisions in surgery planning, and ML solutions without shape analysis will have very limited clinical applicability. Pancreatic cancer is among the deadliest cancers in the world. Currently, surgical removal of a pancreas segment/ whole pancreas is the most common treatment for pancreatic cancer. Pancreatic surgery planning is non-trivial as pancreatic tumors are usually very aggressive and rapidly grow into inoperable stages. Up to 40% of patients with pancreatic tumors undergo exploratory surgeries that aim to identify tumor resectability. The Ph.D. fellow will research how shape analysis can be used for the analysis of pancreatic images. This will include segmentation of pancreas subpart and pacratic vasculature and using this information to plan tumor resections. The fellow will learn how to apply AI for solving real-life clinical challenges and evaluate and document the obtained result. This work will be executed in close collaboration with the Rigshospitalet and potentially Siemens Denmark. b) Inflammatory bowel disease, encompassing Crohn’s disease and ulcerative colitis, are chronic and lifelong diseases of the gastrointestinal tract. Despite state-of-the-art treatment, patients may experience severe symptoms such as bloody diarrhoea, severe stomach pain and malnutrition, all leading to significantly reduced quality of life. Furthermore, approx. 30 % of the patients will undergo the necessary step to remove parts of their bowel without curing the disease. Timely initiation and proper treatment stratification have been deemed the key to breaking their disease course and reducing the number of patients with severe disease courses (e.g. need for surgery, hospitalisation or use of side-effect heavy drugs). We hypothesise that the combination of state-of-the-art machine learning and clinical experience can construct new applications for doctors around the world to help these patients live a more normal daily life. We are looking for committed candidates who are not afraid of asking questions, can cut through in a busy day, and can independently drive a project until the models are applied. In addition to the PhD position, we offer close supervision and help from both experts in machine learning and clinical medical experts with an understanding of data science. This work will be executed in close collaboration with the Hvidovre Hospital. Who are we looking for? In collaboration with Hvidovre Hospital and Rigshospitalet, Denmark, DIKU is seeking a motivated candidates for PhD position in applied machine learning. The aim of the project is to develop new machine learning solutions and apply them for gastrointestinal diseases analysis. The candidate is expected to have knowledge in computer science, software development proficiency and experience in machine learning. Our group and research- and what do we offer? The PhD project will be hosted by the IMAGE section, which performs research in medical image analysis, machine learning, and computer vision. Our section often works with Rigshospitalet and industry partners, so we aim to develop top-level machine learning solutions with a focus on practical applicability. The section is a part of Department of Computer Science, Faculty of SCIENCE, University of Copenhagen. We are located in Copenhagen. The supervisor for the positions – Dr. Bulat Ibragimov – is an Associate Professor working in medical image analysis, computer-aided interventions planning, human-AI interaction, and related fields. He has strong collaborative ties with various Danish and international research institutions. The PhD candidates will be asked to spend 3 months of their study at one of such institutions. We offer creative and stimulating working conditions in dynamic and international research environment. Principal supervisor is Bulat Ibragimov, Associate Professor, Department of Computer Science, email: bulat@di.ku.dk . Technical co-supervisor is Christian Igel, Professor, Department of Computer Science, email: igel@di.ku.dk. Clinical co-supervisor is Martin Hylleholt Sillesen, Department of Organ Surgery and Transplantation, Rigshospitalet, email : martin.hylleholt.sillesen@regionh.dk. The PhD programme Depending of your level of education, you can undertake the PhD programme as either: Option A: A three year full-time study within the framework of the regular PhD programme (5+3 scheme), if you already have an education equivalent to a relevant Danish master’s degree. Option B: An up to five year full-time study programme within the framework of the integrated MSc and PhD programme (the 3+5 scheme), if you do not have an education equivalent to a relevant Danish master´s degree – but you have an education equivalent to a Danish bachelors´s degree. Option A: Getting into a position on the regular PhD programme Qualifications needed for the regular programme To be eligible for the regular PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the project, e.g. Computer Science, Mathematics, or Biomedical Engineering. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database. Terms of employment in the regular programme Employment as PhD fellow is full time and for maximum 3 years. Employment is conditional upon your successful enrolment as a PhD student at the PhD School at the Faculty of SCIENCE, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant. The terms of employment and salary are in accordance to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State (AC). The position is covered by the Protocol on Job Structure. Option B: Getting into a position on the integrated MSc and PhD programme Qualifications needed for the integrated MSc and PhD programme If you do not have an education equivalent to a relevant Danish master´s degree, you might be qualified for the integrated MSc and PhD programme, if you have an education equivalent to a relevant Danish bachelor´s degree. Here you can find out, if that is relevant for you: General assessments for specific countries and Assessment database. Terms of the integrated programme To be eligible for the integrated scholarship, you are (or are eligible to be) enrolled at one of the faculty’s master programmes in Computer Science, Mathematics, or Biomedical Engineering. Students on the integrated programme will enroll as PhD students simultaneously with completing their enrollment in this MSc degree programme. The duration of the integrated programme is up to five years, and depends on the amount of credits that you have passed on your MSc programme. For further information about the study programme, please see: www.science.ku.dk/phd, “Study Structures”. Until the MSc degree is obtained, (when exactly two years of the full 3+5 programme remains), the grant will be paid partly in the form of 48 state education grant portions (in Danish: “SU-klip”) plus salary for work (teaching, supervision etc.) totaling a workload of at least 150 working hours per year. A PhD grant portion is currently DKK 6,397 before tax. When you have obtained the MSc degree, you will transfer to the salary-earning part of the scholarship for a period of two years. At that point, the terms of employment and payment will be according to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State (AC). The position is covered by the Protocol on Job Structure. Responsibilities and tasks in both PhD programmes. Complete and pass the MSc education in accordance with the curriculum of the MSc programme (ONLY when you are attending the integrated MSc and PhD programme) Carry through an independent research project under supervision Complete PhD courses corresponding to approx. 30 ECTS / ½ FTE Participate in active research environments, including a stay at another research institution, preferably abroad Teaching and knowledge dissemination activities Write scientific papers aimed at high-impact journals Write and defend a PhD thesis on the basis of your project We are looking for the following qualifications: Professional qualifications relevant to the PhD project Relevant publications Relevant work experience Other relevant professional activities Curious mind-set with a strong interest in machine learning Good language skills Application and Assessment Procedure Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below. Please include: 1. Motivated letter of application (max. one page) 2. Your motivation for applying for the specific PhD project/State which PhD project you are applying for. 3. Curriculum vitae including information about your education, experience, language skills and other skills relevant for the position 4. Original diplomas for Bachelor of Science or Master of Science and transcript of records in the original language, including an authorized English translation if issued in another language than English or Danish. If not completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted. 5. Publication list (if possible) 6. Reference letters (if available) Application deadline The deadline for applications is 15 October 2022, 23:59 GMT +2. We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements. The further process After deadline, a number of applicants will be selected for academic assessment by an unbiased expert assessor. You are notified, whether you will be passed for assessment. The assessor will assess the qualifications and experience of the shortlisted applicants with respect to the above mentioned research area, techniques, skills and other requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/ Interviews with selected candidates are expected to be held in week 44-45. Questions For specific information about the PhD fellowship, please contact the principal supervisor. General information about PhD study at the Faculty of SCIENCE is available at the PhD School’s website: https://www.science.ku.dk/phd The University of Copenhagen wishes to reflect the surrounding community and invites all regardless of personal background to apply for the position. APPLY NOW Part of the International Alliance of Research Universities (IARU), and among Europe’s top-ranking universities, the University of Copenhagen promotes research and teaching of the highest international standard. Rich in tradition and modern in outlook, the University gives students and staff the opportunity to cultivate their talent in an ambitious and informal environment. An effective organisation – with good working conditions and a collaborative work culture – creates the ideal framework for a successful academic career. Info Application deadline: 15-10-2022 Employment start: 01-01-2023 Working hours: Full time Department/Location: Datalogisk Institut If you apply for this position please say you saw it on Computeroxy Apply

PhD fellowships in Machine Learning in Medicine

PhD Project in computer-aided abdominal procedures
Department of Computer Science
Faculty of SCIENCE
University of Copenhagen

Department of Computer Science (DIKU) invites applicants for PhD fellowships in Machine Learning in Medicine. This project is a part of a collaboration between DIKU and Rigshospitalet, and financed by Novo Nordisk Foundation.

Start date is (expected to be) 1 January 2023 or as soon as possible thereafter.

The project

Funding for two Ph.D. positions working on two projects is available. One of the positions is the development of shape-based machine learning with the application on pancreatic image analysis and surgery planning. The second position is on the development of diagnostic machine learning solutions for classification and prognostication of gastrointestinal diseases. The Ph.D. fellows will work on both theoretical aspects of machine learning solutions development and the application of the developed solutions on clinically relevant problems.

a) Understanding the object shape from an image is an advanced human-level task. Despite extraordinary success of ML solutions in medical image analysis, the inclusion of shape with ML for medical image analysis remains under investigated. However, the shape has been historically playing a critical role in computerized diagnosis. Human organs follow predetermined anatomical patterns, justified by organ functionality and positioning in the human body. Moreover, many diseases do not affect organ shapes, or alter organ shapes in a very predictable manner. Shape models are also used for computer-aided diagnosis, organ morphometry, pathology detection, and inter-organ spatial relationships quantification. Finally, anatomical knowledge is the foundation of physicians’ decisions in surgery planning, and ML solutions without shape analysis will have very limited clinical applicability.

Pancreatic cancer is among the deadliest cancers in the world. Currently, surgical removal of a pancreas segment/ whole pancreas is the most common treatment for pancreatic cancer. Pancreatic surgery planning is non-trivial as pancreatic tumors are usually very aggressive and rapidly grow into inoperable stages. Up to 40% of patients with pancreatic tumors undergo exploratory surgeries that aim to identify tumor resectability. The Ph.D. fellow will research how shape analysis can be used for the analysis of pancreatic images. This will include segmentation of pancreas subpart and pacratic vasculature and using this information to plan tumor resections. The fellow will learn how to apply AI for solving real-life clinical challenges and evaluate and document the obtained result. This work will be executed in close collaboration with the Rigshospitalet and potentially Siemens Denmark.

b) Inflammatory bowel disease, encompassing Crohn’s disease and ulcerative colitis, are chronic and lifelong diseases of the gastrointestinal tract. Despite state-of-the-art treatment, patients may experience severe symptoms such as bloody diarrhoea, severe stomach pain and malnutrition, all leading to significantly reduced quality of life. Furthermore, approx. 30 % of the patients will undergo the necessary step to remove parts of their bowel without curing the disease.

Timely initiation and proper treatment stratification have been deemed the key to breaking their disease course and reducing the number of patients with severe disease courses (e.g. need for surgery, hospitalisation or use of side-effect heavy drugs). We hypothesise that the combination of state-of-the-art machine learning and clinical experience can construct new applications for doctors around the world to help these patients live a more normal daily life.

We are looking for committed candidates who are not afraid of asking questions, can cut through in a busy day, and can independently drive a project until the models are applied. In addition to the PhD position, we offer close supervision and help from both experts in machine learning and clinical medical experts with an understanding of data science. This work will be executed in close collaboration with the Hvidovre Hospital.

Who are we looking for?

In collaboration with Hvidovre Hospital and Rigshospitalet, Denmark, DIKU is seeking a motivated candidates for PhD position in applied machine learning. The aim of the project is to develop new machine learning solutions and apply them for gastrointestinal diseases analysis. The candidate is expected to have knowledge in computer science, software development proficiency and experience in machine learning.

Our group and research- and what do we offer?

The PhD project will be hosted by the IMAGE section, which performs research in medical image analysis, machine learning, and computer vision. Our section often works with Rigshospitalet and industry partners, so we aim to develop top-level machine learning solutions with a focus on practical applicability. The section is a part of Department of Computer Science, Faculty of SCIENCE, University of Copenhagen. We are located in Copenhagen.

The supervisor for the positions – Dr. Bulat Ibragimov – is an Associate Professor working in medical image analysis, computer-aided interventions planning, human-AI interaction, and related fields. He has strong collaborative ties with various Danish and international research institutions. The PhD candidates will be asked to spend 3 months of their study at one of such institutions.

We offer creative and stimulating working conditions in dynamic and international research environment.

Principal supervisor is Bulat Ibragimov, Associate Professor, Department of Computer Science, email: bulat@di.ku.dk .

Technical co-supervisor is Christian Igel, Professor, Department of Computer Science, email: igel@di.ku.dk.

Clinical co-supervisor is Martin Hylleholt Sillesen, Department of Organ Surgery and Transplantation, Rigshospitalet, email : martin.hylleholt.sillesen@regionh.dk.

The PhD programme

Depending of your level of education, you can undertake the PhD programme as either:

Option A: A three year full-time study within the framework of the regular PhD programme (5+3 scheme), if you already have an education equivalent to a relevant Danish master’s degree.

Option B: An up to five year full-time study programme within the framework of the integrated MSc and PhD programme (the 3+5 scheme), if you do not have an education equivalent to a relevant Danish master´s degree – but you have an education equivalent to a Danish bachelors´s degree.

Option A: Getting into a position on the regular PhD programme

Qualifications needed for the regular programme

To be eligible for the regular PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the project, e.g. Computer Science, Mathematics, or Biomedical Engineering. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database.

Terms of employment in the regular programme

Employment as PhD fellow is full time and for maximum 3 years.

Employment is conditional upon your successful enrolment as a PhD student at the PhD School at the Faculty of SCIENCE, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant.

The terms of employment and salary are in accordance to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State (AC). The position is covered by the Protocol on Job Structure.

Option B: Getting into a position on the integrated MSc and PhD programme

Qualifications needed for the integrated MSc and PhD programme

If you do not have an education equivalent to a relevant Danish master´s degree, you might be qualified for the integrated MSc and PhD programme, if you have an education equivalent to a relevant Danish bachelor´s degree. Here you can find out, if that is relevant for you: General assessments for specific countries and Assessment database.

Terms of the integrated programme

To be eligible for the integrated scholarship, you are (or are eligible to be) enrolled at one of the faculty’s master programmes in Computer Science, Mathematics, or Biomedical Engineering.

Students on the integrated programme will enroll as PhD students simultaneously with completing their enrollment in this MSc degree programme.

The duration of the integrated programme is up to five years, and depends on the amount of credits that you have passed on your MSc programme. For further information about the study programme, please see: www.science.ku.dk/phd, “Study Structures”.

Until the MSc degree is obtained, (when exactly two years of the full 3+5 programme remains), the grant will be paid partly in the form of 48 state education grant portions (in Danish: “SU-klip”) plus salary for work (teaching, supervision etc.) totaling a workload of at least 150 working hours per year.

A PhD grant portion is currently DKK 6,397 before tax.

When you have obtained the MSc degree, you will transfer to the salary-earning part of the scholarship for a period of two years. At that point, the terms of employment and payment will be according to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State (AC). The position is covered by the Protocol on Job Structure.

Responsibilities and tasks in both PhD programmes.

Complete and pass the MSc education in accordance with the curriculum of the MSc programme (ONLY when you are attending the integrated MSc and PhD programme)
Carry through an independent research project under supervision
Complete PhD courses corresponding to approx. 30 ECTS / ½ FTE
Participate in active research environments, including a stay at another research institution, preferably abroad
Teaching and knowledge dissemination activities
Write scientific papers aimed at high-impact journals
Write and defend a PhD thesis on the basis of your project

We are looking for the following qualifications:

Professional qualifications relevant to the PhD project
Relevant publications
Relevant work experience
Other relevant professional activities
Curious mind-set with a strong interest in machine learning
Good language skills

Application and Assessment Procedure

Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.

Please include:

1. Motivated letter of application (max. one page)

2. Your motivation for applying for the specific PhD project/State which PhD project you are applying for.

3. Curriculum vitae including information about your education, experience, language skills and other skills relevant for the position

4. Original diplomas for Bachelor of Science or Master of Science and transcript of records in the original language, including an authorized English translation if issued in another language than English or Danish. If not completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted.

5. Publication list (if possible)

6. Reference letters (if available)

Application deadline

The deadline for applications is 15 October 2022, 23:59 GMT +2.

We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.

The further process

After deadline, a number of applicants will be selected for academic assessment by an unbiased expert assessor. You are notified, whether you will be passed for assessment.

The assessor will assess the qualifications and experience of the shortlisted applicants with respect to the above mentioned research area, techniques, skills and other requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/

Interviews with selected candidates are expected to be held in week 44-45.

Questions

For specific information about the PhD fellowship, please contact the principal supervisor.

General information about PhD study at the Faculty of SCIENCE is available at the PhD School’s website: https://www.science.ku.dk/phd

The University of Copenhagen wishes to reflect the surrounding community and invites all regardless of personal background to apply for the position.

APPLY NOW

Part of the International Alliance of Research Universities (IARU), and among Europe’s top-ranking universities, the University of Copenhagen promotes research and teaching of the highest international standard. Rich in tradition and modern in outlook, the University gives students and staff the opportunity to cultivate their talent in an ambitious and informal environment. An effective organisation – with good working conditions and a collaborative work culture – creates the ideal framework for a successful academic career.