Job opening in Applied and Computational Topology advertised by the Queen Mary, University of London

From Michael Farber:

Dear colleagues,

I want to attract your attention to the job opening in Applied and Computational Topology advertised by the Queen Mary, University of London (Lecturer/Senior Lecturer in Applied and Computational Topology - QMUL13442), see

https://webapps2.is.qmul.ac.uk/jobs/job.action?jobID=2940

The salary range is £40,865 - £60,109.

Closing date: 11 January, 2018.

PhD studentship at KTH Royal Institute of Technology

https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:181027/type:job/where:4/apply:1

Doctoral student in Machine Learning
KTH Royal Institute of Technology, School of Computer Science and Communication

KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as in architecture, industrial management, urban planning, history and philosophy.

Department information

KTH Computer Science and Communication (CSC) announces PhD positions in Machine Learning at the department of Robotics, Perception and Learning (RPL) https://www.kth.se/en/csc/forskning/rpl.

Job description

The scientific work will be conducted along either of the following research directions:

1) Geometric and Topological Methods for Machine Learning with applications to Robotics

Topological Data Analysis is a recently emerging sub-branch of machine learning that enables inference about the global structure of datasets based on rigorous mathematical theory with origins in Algebraic Topology. The research under this theme will focus on developing new geometric and topological techniques for machine learning with a focus on potential application areas in robotics. Possible applications of Geometric and Topological Data Analysis in Robotics include reasoning about robot configuration spaces and free space. This could include development of approaches to autonomously detect unsafe configurations in a self-driving car scenario and to understand how compact components of the free space can be utilized to enable new types of robot manipulation interactions by means of the concept of "Caging". A second potential sub-thread is to investigate how Topological Data Analysis may be of use to analyze representations of data determined by Deep Learning Algorithms. The research will be supervised by Florian Pokorny, Assistant Professor at RPL.

2) Data-driven scene understanding and control in Human-Robot collaborative settings

Future robotic applications are believed to a greater extent include collaboration with humans, humans that do not necessarily have a technical background. Interaction between human and robot thus need to be in a manner that the human finds natural. A collaborative robot should adapt to changes in the environment and tasks that are placed on it. It needs to be in constant learning mode and gradually adjust its behaviours given feedback from both its sensory system and the human collaborator. Research under this theme will focus on a combination of deep learning for scene understanding and generative action modelling, with reinforcement learning applied for control. Emphasis will be placed on methods with which a human can teach a robot the best way of solving a particular task, either through demonstration or by physically guiding the robot. The research will be supervised by Mårten Björkman, Associate Professor at RPL.

This is a four-year time-limited position that can be extended up to a year with the inclusion of a maximum of 20% departmental duties, usually teaching. In order to be employed, you must apply and be accepted as a doctoral student at KTH.

Data science (especially topology position at The Henry M. Jackson Foundation

THE HENRY M. JACKSON FOUNDATION FOR THE ADVANCEMENT OF MILITARY MEDICINE

Position Description
Data Scientist

Position No: FLSA Status: Exempt
Grade: EEO Category/Job Group:

JOB SUMMARY We are seeking a Data Scientist to join the Austere environments Consortium for Enhanced Sepsis Outcomes (ACESO). ACESO aims to identify host-based markers capable of accurately diagnosing and prognosing patients with severe infections in austere settings and transitioning those markers to point-of-care assay platforms. The Data Scientist is responsible for analyzing complex data and developing insights through the use of statistical models, data mining, and data visualization techniques.

This position is based at The Henry M. Jackson Foundation (HJF) in Bethesda, Maryland, although alternate arrangements will be considered. HJF provides scientific, technical and programmatic support services for the worldwide ACESO program.

ESSENTIAL JOB DUTIES: 95% of time

1. Analyzes complex datasets including RNA sequence data, proteomic, phosphoproteomic, and metabolomics data. Applies advanced statistical and predictive modeling techniques and data visualization approaches. Develops innovative approaches to answer research questions.

2. Integrates and prepares large datasets, develops specialized database and computing environments as needed.

3. Provides subject matter expertise as needed, including recommendations on data collection and integration.

4. Communicates results on a regular basis with the science team and key stakeholders, and prepares presentations and reports as needed.

5. Performs other duties as required.

JOB SPECIFICATIONS:
Required Knowledge, Skills, and Abilities:
 Experience with complex datasets
 Proficiency in statistical analysis, forcasting/predictive analytics, and algorithm optimization.
 Experience with data mining/pattern recognition approaches; experience with topological data analysis preferred
 Strong programming skills
 Able to develop solutions to loosely defined problems
 Able to communicate effectively

Minimum Education/Training Requirements: PhD in mathematics, statistics, computer science or related field. At least 2 years relevant experience.

Physical Capabilities: Extended periods of sitting

Required Licenses, Certification or Registration: n/a

Supervisory Responsibilities/Controls: May provide guidance to junior analysts

Work Environment: Office or laboratory environment

Any qualifications to be considered as equivalents, in lieu of stated minimums, require the prior approval of the Director of Human Resources