Postdoctoral position in the Grupo de Aprendizaje Automático/Machine Learning Group at UAM (FACIL Project)

The Grupo de Aprendizaje Automático/Machine Learning Group at the Departamento de Ingeniería Informática of the Universidad Autónoma de Madrid (http://arantxa.ii.uam.es/~gaa/ ) offers a postdoctoral position to work on acceleration methods for iterative model learning and their application as well as of other complementary Machine Learning approaches to achieve better predictions in renewable energy.

Candidates should have a recent, machine learning-related PhD in computer science or nearby areas, a solid research record and good programming abilities in Python and C/C++, being familiar with the main Python libraries for scientific computation and Machine Learning such as numpy, scipy, pandas, scikit—learn and others.

The position’s duration will be 18 months starting in principle on April 1st, 2017 and is funded through the project FACIL--Ayudas Fundación BBVA a Equipos de Investigación Científica 2016. Applicants should send a CV and a statement of research interests to jose.dorronsoro AT uam.es. This call will be open and applications reviewed until the position is filled.

PhD Contract for an outstanding candidate to undertake research in multi-agent coordination applied to smart buildings

The expected outcome of the research is a PhD dissertation concerning the development of distributed scalable and robust algorithms to coordinate multiple agents in open large smart buildings. Topics of interest for the research include distributed and decentralized exact and heuristic optimization, multi-agent systems, semantic technologies and data stream processing, among others.

The research will be carried out in a close collaboration between the Centre for Intelligent Information Technologies (CETINIA) at the University Rey Juan Carlos (URJC) and the Department of Informatics and Control Systems (DIA) at the Institute Mines Telecom Lille Douai (IMT Lille Douai).

The candidate will conduct research activities organised as follows:

·       Months 1-18 at the Centre for Intelligent Information Technologies (CETINIA) at URJC.

·       Months 19-36 at the Department of Informatics and Control Systems (DIA) at IMT Lille Douai.

Eligibility criteria

The candidate is expected to have (or be finishing) a Master’s Degree or equivalent in Computer Science, Industrial Engineering, or a related field.

The applicant is expected to have a good background in multi-agent systems, semantic technologies and operations research. Applicants with an experience in one or more of the above academic fields are encouraged to apply. Furthermore, a good knowledge of written and oral English are required, as well as good programming skills, preferably in Java or Matlab. A high level achievement and experience in multi-disciplinary collaboration are an advantage.

Benefits of the contract

The work contract should start in April 2017 and should last for three years, with a gross average salary around €1.700 per month.

How to apply

If you wish to apply for this work contract, you are kindly asked to send your (i) motivation letter describing your research interests and goals, (ii) CV, (iii) transcripts, (iv) certificates, (v) contacts for two references, and (vi) relevant publications (if any) in one PDF file at the following e-mails: Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo. and Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo. by  March 3^rd, 2017.

We will carefully review your application and you will be advised of an outcome in March 2017.

Professor/Associate Professor in machine learning – University of Bergen

The Department of Informatics (http://www.uib.no/en/ii) has up to two vacancies for permanent positions as professor/associate professor in machine learning. If the top applicants do not qualify as full professor, they may be evaluated for an associate professor position. The department will build a strong research group in machine learning. This announcement is a key step towards this goal. Our current research groups, in particular, the groups in bioinformatics and visualization, envision multiple opportunities for collaboration.

Qualifications and personal qualities:

• The applicant must have a doctoral degree in the relevant field.
• The applicant must have postdoctoral experience.
• The applicant need to document strong theory expertise and contributions to practical applications within informatics.
• The successful candidate is expected to play an active role in interdisciplinary collaborative research, training, and education, and contribute to broader research initiatives.
• Research leadership experience as well as experience in coordinating research activities are expected. Candidates should be able to demonstrate strong interpersonal skills and the ability to work and communicate well with others in a team environment.

Key criteria for evaluating candidates will be scientific qualifications, the ability to attract external funding, project management, and interest and experience in teaching and supervising students. We are seeking candidates qualifying for a full professorship and candidates qualifying for an associate professor position. We will offer well-qualified candidates with an excellent research plan a start-up package including one or several recruitment positions (PhD students, postdoctoral candidates). In the case when several highly qualified candidates are identified, up to two candidates may be offered a position, subject to the budget situation.

We can offer:

• A good and professionally challenging working environment
• Salary will be in the range of 74–84 (code 1013) in the state salary scale.
• Starting salary for associate professors is 67 (code 1011/pay framework 24.8). Automatic salary increases will occur at fixed intervals within the pay framework (in the range of 67–72).
• A higher salary may be considered for a particularly well qualified applicant.
• Enrolment in the Norwegian Public Service Pension Fund
• A position in an inclusive workplace (IW)
• Good welfare benefits

Your application must include:

• CV
• A letter of motivation
• At least 2 referees that may be contacted
• A brief research plan for a first five-year period
• Diplomas
• References
• Complete list of publications
• Documentation of your pedagogical qualifications
• List of academic work that the applicant believes should be taken into consideration in the assessment, including information about where this work was published (max. 10).

The application and appendices with certified translations into English or a Scandinavian language must be uploaded at JobbNorge.

Closing date for applications: 1 April 2017

General information:
For further information about the position please contact the Head of Department, professor Petter E. Bjørstad: Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo. / phone: +47 55 58 41 71.

International Postdoctoral Program - 2017

Búsqueda de candidatos para la realización de tesis doctoral

El grupo de investigación Modelización, Optimización e Inferencia Estadística (MODES) de la Universidade da Coruña (UDC) convocará próximamente contratos predoctorales con cargo a fondos de investigación, ya conseguidos por el grupo, y además participará en diversas convocatorias competitivas de contratos predoctorales destinados a personas que quieran realizar la tesis doctoral en el seno del grupo. La cuantía de estos contratos será semejante a la de los contratos FPU del MECD o FPI del MINECO. El grupo MODES forma parte del Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), recientemente distinguido como uno de los cinco únicos centros de investigación singulares de la Comunidad Autónoma Gallega por parte de la Consellería de Cultura, Educación e Ordenación Universitaria de la Xunta de Galicia. Además, varios de los miembros de MODES son investigadores asociados del Instituto Tecnológico de Matemática Industrial (ITMATI).

Los doctorandos se formarán tanto en aspectos metodológicos, en el ámbito de la estadística o la investigación operativa, como en otros avances computacionales, pudiendo también incardinarse en alguno de los proyectos de investigación que el grupo MODES lidera en colaboración con empresas. En el marco de las tesis también se llevarán a cabo colaboraciones con otros grupos nacionales y extranjeros. El grupo MODES ha vuelto a ser reconocido como grupo de referencia competitiva de la Xunta de Galicia en la convocatoria del año 2016. Los miembros del grupo son autores de más de un centenar de publicaciones en el período 2012‐2016 en revistas de alto impacto internacional.

El perfil requerido es el de graduado, licenciado o ingeniero con sólida formación en matemáticas y en estadística, preferiblemente con título de máster o cursándolo y con buen expediente académico. Es necesario estar matriculado o en condiciones de matricularse en un programa de doctorado en 2017 (preferentemente en febrero).

Aquellos interesados deben enviar su expresión de interés, acompañada de copia electrónica de su expediente académico por correo electrónico a la dirección Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.. Deberán hacerlo no más tarde del día 29 de enero de 2017. La expresión de interés tiene formato libre pero ha de indicar, al menos, la formación académica del candidato y su experiencia previa en investigación, si es el caso.

Para más información sobre las líneas del grupo de investigación puede consultarse la web del grupo MODES http://dm.udc.es/modes/, así como contactar con el profesor Ricardo Cao, coordinador del grupo MODES, en el e‐mail Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo., o con cualquier otro investigador del grupo.