Machine Learning for Signal Processing

We are looking for a research engineer in the area of Machine Learning and Deep Neural Networks. You will join our core research team in the Automotive department and help us push the state of the art in Natural Language Processing and related fields, such as Question Answering and Semantic Parsing. You will contribute to the next generation of our in-car voice user interface and virtual assistant.

This position is located in our office in Aachen, Germany. 

Responsibilities: 

• You will learn about and keep yourself up-to-date on state-of-the-art in ML, DNN, NLU and related domains.
• You will design, implement and evaluate new algorithms and contribute to the next generation of our systems.
• You will interact with researchers to create collaborations where possible. You will contribute to other research areas within the company where relevant.
• You will learn about other related technologies within the company (e.g., speech recognition, language modelling, dialog systems) in order to improve the end-to-end solution and create synergies.
• You will help bring your creations to life in our products by interacting with engineering and production teams.
• Perform tasks related to securing and keeping the products, tools, and processes that you are responsible for securing

Qualifications:

• Ideally 2 years of work experience, but promising graduates are encouraged to apply!
• Excellent programming skills in Python and C/C++
• Experience in Deep Learning, preferrably in NLU
• Solid background in mathematics and statistical modeling
• You are a team player and are goal-oriented
• Fluent in English (oral and written)

Preferred skills:

• Experience contributing to open source Deep Learning packages such as Tensorflow
• Knowledge of computational linguistics, ontologies and databases
• Knowledge of related technologies: speech recognition, dialog systems, question answering, etc.
• Knowledge of languages other than English, such as e.g. German, French, Mandarin, …
• User experience with HPC and grid software
• Knowledge of embedded platforms
• Experience in Software Design and Continuous Integration

Education: Master in Computer Science or Computational Linguistics

We are looking for an excellent Senior Research Scientist in the area of Machine Learning and Deep Neural Networks. You will join our core research team in the Automotive department and help us push the state of the art in Natural Language Processing and related fields, such as Emotion Recognition, Question Answering, and Semantic Parsing. You will contribute to the next generation of our in-car voice user interface and virtual assistant.
 

Principal duties and responsibilities:

• You will learn about and keep yourself up-to-date on state-of-the-art in ML, DNN, NLU and related domains.
• You will design, implement and evaluate new algorithms and contribute to the next generation of our systems.
• You will interact with researchers to create collaborations where possible. You will contribute to other research areas within the company where relevant.
• You will learn about other related technologies within the company (e.g., speech recognition, language modelling, dialog systems) in order to improve the end-to-end solution and create synergies.
• You will help bring your creations to life in our products by interacting with engineering and production teams.
• Perform tasks related to securing and keeping the products, tools, and processes that you are responsible for securing

Qualifications: 

• Minimum years of work experience: 2-4
• Excellent knowledge and experience in Deep Learning, preferrably in NLU
• Strong programming skills in Python
• Solid background in mathematics and statistical modeling
• You are a team player and are goal-oriented
• Fluent in English (oral and written)

Preferred skills:

• Experience contributing to open source Deep Learning packages such as Tensorflow
• Knowledge of computational linguistics and related concepts
• Knowledge of related technologies: speech recognition, dialog systems, question answering, emotion recognition, etc.
• Programming skills in C/C++
• Knowledge of languages other than English, such as e.g. German, French, Mandarin, …
• User experience with HPC and grid software
• Knowledge of embedded platforms
• Strong publication record in Artificial Intelligence and Machine Learning as demonstrated by publications in relevant conferences such as AAAI, IJCAI, ICML, etc.

Education: PhD in Machine Learning, NLP, or a related field

We are looking for enthusiastic and talented students to join our
growing international research team at Heinrich Heine University
Düsseldorf.

** Apply by 1st June 2019 **

These PhD positions are fully funded by Prof. Milica Gasic' ERC Staring
Grant project DYMO.  They come with a competitive salary (pay grade
EG13) and no teaching duties.

The goal of the project is to develop the next generation of statistical
dialog systems that are dynamically extensible in terms of their
knowledge, management and generation. The project also involves building
realistic user models and sophisticated reward mechanisms based on
cutting edge machine learning and NLP methods.

The candidate must hold a Masters degree in Computer Science,
Mathematics,  Engineering or a related field. Excellent programming
skills are essential.
An ideal candidate would have some experience with NLP and Machine
Learning.

The candidate should be fluent in English.  Knowledge of German is not
required.

Düsseldorf is a very cosmopolitan city. It is the capital of the largest
German state, nicely positioned on the Rhein river, with a beautiful old
town as well as many other nice newly built quarters.  Düsseldorf
airport is the third largest in Germany and it's only 10min away from
the city centre.

To apply please send your CV and a brief cover letter to Prof. Milica Gasic at gasic@uni-duesseldorf.de. For any questions please contact Prof. Milica Gasic.

As part of the new project Ecce Aqua one fully funded PhD fellowship is available at the University of Padova (Italy) on imaging and big data analytics applied to marine vertebrates (http://www.dottorato.veterinaria.unipd.it/health-aquatic-animals).
We welcome candidates with background in some of the following fields: image analysis, computational microscopy, machine/deep learning, data science and statistics, radiomics.

Closing date: 14-05-2019 13:00

Supervisors and contacts
Enrico Grisan, Department of Information Engineering, enrico.grisan@unipd.it
Livio Corain, Department of Management and Engineering, livio.corain@unipd.it
Bruno Cozzi, Department of Comparative Biomedicine and Food Science, bruno.cozzi@unipd.it

Short description
The overall aim of the project is to develop processing pipelines for the analysis of CT and MRI scans from marine fauna (cetaceans, turtles, fishes) with a specific emphasis on the study cetacean brain. Additional ex-vivo histological data will be available to provide an integrated digital imaging approach to comparative anatomy. The PhD student will be part of a highly multidisciplinary teams composed by computer scientists, engineers, statisticians, biologists and neuroanatomists.

The PhD program is designed as a strongly interdisciplinary research environment that is made up of three overlapping layers: comparative anatomy, imaging and data science for big data analytics. The comparative anatomy of marine mammals represents the main layer at the crossroads between imaging and data analysitics. Imaging and data science play a synergic role in handling big data under multi-faceted circumstances and goals, focusing on 3D/2D reconstruction and interpretation of imaging information. Data science aims at developing innovative inference-based methods to investigate possible (small) effects due to factors such as species, sexual dimorphism, aging and pathology.

The topics that can define the doctoral project are:
Imaging:
o        3D shape reconstruction and registration of multimodal data
o        2D to 3D image registration and visualization
o        Artificial intelligence for the segmentation and classification of histological images
Big Data Analytics:
o        Web-based apps development and set-up of a tissue digital database
o        Multivariate testing and ranking methods
o        Parallel programming for efficient randomization algorithms

Application procedure
Candidates must apply online at https://pica.cineca.it/unipd/dottorati35
selecting the PhD position labelled as: Progetto "ECCE AQUA" - Topic: Tissues images analyses by using big data management and evaluation;

Instructions - Help
 

We have 2 PhD Positions open in Distributed Statistical Learning in IoT with Adversarial Environments

Job Description

In the emerging paradigms of cyberphysical systems (CPS) and internet-of-things (IoT), large quantities of data are constantly collected by numerous sensors that are often geographically dispersed or, to make the matters even more challenging, mobile. This makes concentrating the data at a central processing hub unfeasible due to constraints imposed on the network infrastructure, e.g., the energy budgets of the sensors, as well as the capacity of the communication channels, and security requirements. Thus, distributed processing of data over networks of machines/agents is essential for solving inference problems related to many CPS/IoT-based applications.

Current methods for distributed inference and learning are not adapted for operation in IoT systems with malicious adversaries, vulnerable to data falsification and susceptible to privacy leakage. The goal of this project is to develop distributed signal processing and machine learning algorithms that preserve privacy and are resilient to malicious disturbances, while still maintaining the operational goal of the IoT system, i.e., to effectively convert the data collected by myriads of agents/sensors into actionable intelligence.

Qualification requirements

 We seek two highly-motivated individuals who have

• strong background in mathematics and signal processing, and a research-oriented master thesis in a related field, e.g., statistical signal processing, information theory, statistical machine learning, multi-agent networked systems, applied mathematics, or optimization
• experience with programming
• good written and oral English language skills

Publication activities in the aforementioned disciplines will be considered an advantage but is not a requirement.

Salary and conditions

PhD candidates are remunerated in code 1017, and are normally remunerated at gross from NOK 449 400 before tax per year. From the salary, 2 % is deducted as a contribution to the Norwegian Public Service Pension Fund.

Application submission

For more information on the application submission and a detailed list of required documents, please follow the link Distributed Learning in IoT with Adversarial Environments.

https://nato.taleo.net/careersection/2/jobdetail.ftl?job=190339&lang=en#.XKyV6uNspCw.gmail 

Deputy Director-190339

Primary Location

 Italy-La Spezia

NATO Body

 Centre for Maritime Research and Experimentation (CMRE)

Schedule

 Full-time

Euro (EUR) Yearly

Grade A.6

1.    GENERAL JOB DESCRIPTION

1.1       POST IDENTIFICATION

 Primary Location: Italy – La Spezia

NATO Body: Centre for Maritime Research and Experimentation (CMRE)

Schedule: Full-time

Application Deadline: 8 May 2019

Salary (Pay Basis) 10.371,70 (EUR) Monthly

Grade: A6

Clearance Level: Cosmic Top Secret

1.2       POST CONTEXT

The Centre for Maritime Research and Experimentation (CMRE) is an executive body of the Science and Technology Organization (STO) and is governed by the NATO Science and Technology Board (STB) under the provisions of the STO Charter. Within the framework of the STO in-house delivery business model, the CMRE organizes and conducts scientific research and technology development and delivers innovative, field-tested S&T solutions to address the defence and security needs of the Alliance.

The CMRE operates in a challenging international environment, interacting with a broad range of stakeholders from military, political and scientific organizations.

The Deputy Director is the second in authority of the Centre.  All the administrative offices of the Centre report to the Deputy Director including: Security, Health and Safety, Business Support Office, Human Resources, Budget and Finance, Purchasing and Contracting, and General Services. 

1.3       MAIN ACCOUNTABILITIES

The incumbent will perform primarily duties such as the following:  

• Represents the Director as required in his/her absence

• Be responsible for the efficient and safe operation of the CMRE on a day-to-day basis

• Lead the development of the annual Financial Plan.  Develop and forecast multi-year human resources, financial, and investment plans required to position the Centre to reach its business and operational goals

• Lead Strategic Planning for the Centre including an annual five-year strategic plan that will define the expected outlook of Centre involvement and describe how the Centre competency areas, resources, and tools should develop to best support this engagement

• Lead the formulation and monitor the execution of the CMRE’s annual programme of work in coordination with Head Research, Head Engineering and Information Technology, and the Business Support Office

• Identify, resolve, and manage cross-organizational programmatic and resource prioritisation issues while balancing sponsor satisfaction with the delivery of coherent capabilities to NATO and other customers

• Maintain knowledge of technological developments and resource management best practices relevant to the work of the CMRE

• Represent CMRE’s scientific and technological objectives and accomplishments to other S&T agencies, industry, academia, government organizations, the international community, and the general public

• Promote CMRE activities to NATO, S&T, armaments, and operational counterparts of allied nations, Industry and other stakeholders

• Develop and negotiate joint research projects, cooperative and partnership agreements, and manage international collaboration programmes with the Business Support Office

• Provide guidance and advice to the scientific departments on CMRE’s strategic directives and the vision of CMRE’s programme of work

• The incumbent is an agent for change in the CMRE culture, business processes, and procedures

1.4       ADDITIONAL DUTIES

• The incumbent may be required to perform his or her duties onboard Centre vessels and may be called upon to perform like duties elsewhere in the organization.

• Flexibility Clause:  In order for the organization to deal with emergent requirements, the incumbent may be required to perform other related duties as directed (in particular, the incumbent can expect to work as a member of Working Groups, Project Teams, etc. for defined periods of time). 

• Annual TDY Requirement:  The incumbent can expect to go on TDY both within and outside NATO’s boundaries

   

2        QUALIFICATION AND EXPERIENCE

2.1       ESSENTIAL QUALIFICATIONS

• We are looking for a candidate with a PhD degree in scientific, engineering, or information or related technology field and 12 years post-related experience; or a Master (MSc)/MA degree in a related discipline with 15 years post-related experience.

• Experience in leading successful customer-funded organizations.

• Experience in research related to the maritime environment, systems, or operations.

• Senior level management experience of an S&T lab or team that has brought innovation in the maritime/naval domain.

• Management experience in planning, programming, and budgeting at various levels.

• Experience in the leadership and management of a highly qualified workforce and sophisticated technological assets in S&T organizations of recognized national or international standing.

• Practical experience in the conduct of international relations and demonstrated ability to work effectively in an international environment with scientific and/or military personnel at all levels.

• Knowledge of military capability development processes.

• Broad knowledge of military technology.

• Demonstrate strategic and innovative thinking.

• Demonstrate experience in change leadership.

• Practical experience in security matters.

• Knowledge of information management.

• English SLP 4444 (Listening, Speaking, Reading and Writing).

3        DESIRABLE QUALIFICATIONS

•  Advanced training in management or an equivalent cross-functional management experience.

• Experience with accountability to government administrators.

• Leadership in the defence sector.

• Demonstrated record of leading successful organizational transformation.

• Experience of quality management principles (e.g., ISO 9001).

• Knowledge of project management software tools.

• Knowledge of additional languages.

• Experience working with Italian civilian and military organizations and authorities.

4        COMPETENCIES

Competencies required:

• Leadership

• Change Leadership

• Analytical Thinking

• Developing Others

• Customer Service Orientation

• Initiative

• Organizational Awareness

• Empathy

5        INTERRELATIONSHIPS

• The post reports to the Director, CMRE and will direct the work of managers and team members within the Section.

   

  • Direct reports: 7 - 10

     

• The incumbent may often be involved in significant discussions at senior committee level with representatives from NATO nations or other NATO bodies on behalf of the CMRE. Has a regular professional contact typically at senior management level inside and outside the Centre. Develops policy and processes which require explanation, discussion, persuasion and approval of actions.

6          WORK ENVIRONMENT

• The work is normally performed in a typical Office environment.

• The nature of this position may require the staff member at times to be called upon to travel for work and/or to work outside normal office hours.

7        WHAT DO WE OFFER

• A world-class research facility located in the sea port of La Spezia, Italy supported by two specialised research vessels. 

• An exciting place in which to work situated at an ideal location, the port of La Spezia, Italy, enabling synergy with regional and global academic institutes and industry.

• Salary and conditions of employment will be in accordance with the NATO Civilian Personnel Regulations (NCPR), which includes a rewarding salary and a comprehensive system of allowances, supplements and insurances to support families and, in case of expatriated staff, offers an interesting “expatriate” package. 

• A generous annual leave and, (where eligible) home leave.

• The successful candidate will be offered a three years’ definite duration contract, which may be renewed for subsequent periods subject to business needs, satisfactory performance and the need to rotate skills and talent within the Organization.

• Applicants who are not successful in this competition may be offered an appointment to another post of a similar nature, albeit at the same or a lower grade, provided they meet the necessary requirements.

8        RECRUITMENT PROCESS

• Please note that we can only accept applications from nationals of NATO member countries.

• Applications (including an electronic version of the CV, the most relevant publications, the diplomas – stating the highest level of education) for this vacancy are to be submitted using the E-recruitment system.

• Appointment will be subject to receipt of a security clearance (provided by the national Authorities of the selected candidate) and approval of the candidate’s medical file by the CMRE Medical Adviser.

• The deadline for receiving the applications is the 8th of May2019.

• The shortlisted candidates will undergo an Assessment Centre which will take place in Belgium (Brussels). Assessment Centre and final interview will be scheduled in the week of the 8th of July 2019.

9        ADDITIONAL INFORMATION:

• CMRE values diverse backgrounds and perspectives and is committed to recruiting and retaining a diverse and talented workforce. We welcome applications of nationals from all Member States and strongly encourage women to apply.

• Selected candidates are expected to be role models of integrity, and to promote good governance through ongoing efforts in their work.

For any queries, please contact CMRE Recruitment Team at: recruitment@cmre.nato.int  

To learn more about the CMRE and our work, please visit our website: www.cmre.nato.int

CTTC is seeking to hire an Early Stage Researcher (ESR) to join the WindMill project. The project offers an excellent research and training programme:

• Opportunity to join a network of leading Universities, research institutes and companies in the field of wireless communications and machine learning.
  • The ESR is primarily hosted by CTTC with stays in partner institutions (secondments).
  • Training programme including regular summer and winter schools to build technical skills as well as soft skills.
  • The salaries are very competitive and complemented by mobility or family allowance.

The ESR will be enrolled in the Signal Theory and Communications PhD programme at Universitat Politècnica de Catalunya (UPC). Further information about the PhD project is below:

Candidate Profile:

1. An undergraduate degree and Master’s degree (or equivalent) in Electrical Engineering or Computer Science. Strong background in mathematics is required. Background in wireless communications and/or machine learning is an advantage.
2. Excellent written and verbal communication, as well as presentation skills.
3. Highly proficient English language skills.
4. Excellent organisational skills, creative, innovative, independent thinker
5. Motivation to collaborate in an interdisciplinary international team.
6. Motivation to participate in training programs.
7. Ability to travel and work in and outside Europe.

Eligibility Requirements

All candidates must meet the following requirements to be considered for this post:

a)       Early-Stage Researchers (ESRs) shall at the time of recruitment by the host organisation be in the first four years (full-time equivalent research experience) of their research careers and not yet have been awarded a doctoral degree. Full-time equivalent research experience is measured from the date when a researcher obtained the degree which would formally entitle him or her to embark on a doctorate, either in the country in which the degree was obtained or in the country in which the researcher is recruited.

b)      At the time of recruitment by the host organisation, researchers must not have resided or carried out their main activity (work, studies, etc.) in the country of their host organisation for more than 12 months in the three years immediately prior to the recruitment date. Compulsory national service, short stays such as holidays and time spent as part of a procedure for obtaining refugee status (under the 1951 Geneva Convention and the 1967 Protocol) are not taken into account.

Further information and applications at: http://www.cttc.es/career/call-5-2019-1/

Many applications generate large data sets from which information needs to be extracted. The emerging field of structured data science extends signal processing to data science. 

Context

Retrieving useful information out of large data sets is receiving an increasing amount of attention these days. Emerging applications like autonomous driving, the square kilometre array for radio astronomy, biomedical sensing systems, the internet-of-things with millions of users and sensors, all generate enormous amounts of data and it is not always clear how to gather, process, and analyze that data in an efficient and rigorous manner. Data science provides a solution to this problem. It basically yields a set of tools for data mining, data cleansing, machine learning and data analysis. 

While these tools can tackle a wide variety of problems, computer science approaches often ignore the structure that is present in the problem due to the physics. This structure could come from the prior knowledge of the model that generates the data (e.g., radio frequency channel models, biomedical signal models, diffusion models), or it could directly relate to the structure in the data (e.g., space-time, sparsity, network/graph data). Taking such structure into account will aid many of the existing data science tools, making them easier to interpret and simpler to implement. This field of structured data science is shaped by the nontrivial interplay between conventional signal processing and conventional data science. The goal is to illuminate and explore this interplay and apply it to the earlier mentioned applications.

Requirements

The opening for an Assistant Professor at TU Delft is intended to further develop this area. A background in statistical signal processing/modelling and the ability to apply this to data science/machine learning is required. Generally we search for candidates with a strong signal processing background complementary to the expertise that is already present in the CAS group. 

The candidate will also be involved in teaching and e.g. develop new courses on structured data science and machine learning for Electrical Engineering students.

While this position is defined as a tenure-track Assistant Professor position, excellently qualified but more senior researchers are also invited to apply.

Candidates should have (1) a PhD degree in Electrical Engineering or a closely related disciplne, with outstanding academic credentials, (2) several years of working experience as a Postdoctoral Researcher in an academic institution, and (3) the ambition to be a future scientific leader in the mentioned area.

Please announce your interest to Mrs. Minke van der Put (M.J.vanderPut@tudelft.nl) by submitting your CV, cover letter and contact details. Clearly indicate the related area of interest (i.e., structured data science), as this opening is part of a wider search for new faculty.

Contact Prof. Geert Leus for informal information. 

This is a great opportunity for recent postdocs that are AI enthusiasts. Knowledge in the area of material sciences is not mandatory (see the link with the job offer). So, if you are working on AI methods, but have not been working with material sciences yet, but would like to acquaint yourself with the topic, feel encouraged to apply. The deadline has been extended to March 26th. http://www.pse.kit.edu/downloads/stellenangebote/W1-KIMAT-19-03-10-engl.pdf.

There is a postdoctoral scholar opportunity at the University of Southern
California (USC) in a multidisciplinary research area involving
sample/learning complexity in machine learning; optimization theory;
information theory; and graph signal processing. More specifically, we are
looking for a candidate with a strong theoretical foundation in some of these
areas while being motivated to apply the theory to practical datasets using
tools such as deep neural networks. The successful candidate will join a
multidisciplinary research team led by Professors Avestimehr, Ortega,
Soltanolkotabi, and Diakonikolas at ECE and CS departments of USC.
 
Interested candidates should contact us via email (avestimehr@ee.usc.edu [1])
by sending (1) CV, and (2) a brief (1-page) research statement.