We are on a mission to make the journey remarkable by bringing the ease of
digital transactions to the physical world with mobility commerce, a new
way to transact that connects transportation, payments, and local
businesses for the first time. That means making bricks and mortar smarter,
and making living and playing in our cities a joy.
Open Roles
 
Machine Learning Software Engineer
<https://www.karkidi.com/job-details/36658-machine-learning-software-engineer-job>
 
United States
 
Senior Embedded Systems Engineer
<https://www.karkidi.com/job-details/36659-senior-embedded-systems-engineer-job>
 
Seattle, WA, USA
 
Senior Technical Program Manager – Imaging
<https://www.karkidi.com/job-details/36660-senior-technical-program-manager-imaging-job>
 
Seattle, WA, USA; Los Angeles, CA, USA; New York, NY, USA
 
Senior Manager, Data Analytics
<https://www.karkidi.com/job-details/36661-senior-manager-data-analytics-job>
 
Seattle, WA, USA; Los Angeles, CA, USA; New York, NY, USA
 
Senior Data Engineer
<https://www.karkidi.com/job-details/36662-senior-data-engineer-job>
 
Seattle, WA, USA; Los Angeles, CA, USA; New York, NY, USA
 
Senior Data Architect (DW and ETL)
<https://www.karkidi.com/job-details/36663-senior-data-architect-dw-and-etl-job>
 
Los Angeles, CA, USA; New York, NY, USA; United States
 
Senior Director of Pricing Strategy
<https://www.karkidi.com/job-details/36664-senior-director-of-pricing-strategy-job>
 
Austin, TX, USA; Detroit, MI, USA; Houston, TX, USA
 
Senior Director of Pricing Strategy
<https://www.karkidi.com/job-details/36665-senior-director-of-pricing-strategy-job>
 
Los Angeles, CA, USA; Nashville, TN, USA
 
Senior Director of Pricing Strategy
<https://www.karkidi.com/job-details/36666-senior-director-of-pricing-strategy-job>

The Machine Learning Team at the National Institute of Mental Health (NIMH)
in Bethesda, MD, has an open position for a machine learning research
scientist. The NIMH is the leading federal agency for research on mental
disorders and neuroscience, and part of the National Institutes of Health
(NIH). A copy of this ad may be found at
 
https://nih-fmrif.github.io/ml/index.html
 
### About the NIMH Machine Learning Team
 
Our mission is to help NIMH scientists use machine learning methods to
address a diverse set of research problems in clinical and cognitive
psychology and neuroscience. These range from identifying biomarkers for
aiding diagnoses to creating and testing models of mental processes in
healthy subjects. Our overarching goal is to use machine learning to
improve every aspect of the scientific effort, from helping discover or
develop theories to generating actionable results.
 
We work with many different data types, e.g. very large brain imaging
datasets in various imaging modalities, neural recordings, behavioral data,
and picture and text corpora. We have excellent computational resources,
both of our own (tens of high-end GPUs for deep learning, several large
servers) and shared within the NIH (a cluster with hundreds of thousands of
CPUs, and hundreds of GPUs).
 
As a machine learning research group, we develop new methods and publish in
the main machine learning conferences (e.g. NeurIPS and ICLR), as well as
in psychology and neuroscience journals. Many of our problems require
devising research approaches that combine imaging and non-imaging data, and
leveraging structured knowledge resources (databases, scientific
literature, etc) to generate explanations and hypotheses. You can find more
about our work and recent publications at
 
https://cmn.nimh.nih.gov/mlt
 
### About the position
 
This position requires experience in the use of deep learning in the
context of substantial research projects, ideally having led to
publications (or preprints). As our team works on both applications and
method development, here are some examples of projects we have carried out
or are presently engaged in:
 
– Bayesian deep neural networks for brain segmentation with uncertainty
– convolutional neural networks on structural or functional brain MRI for
decoding information or person characteristics
– a comparison of approaches for generating gradient-based saliency maps
for neural networks in brain imaging data
– a method for distributed training and consolidation of Bayesian deep
neural networks
– modifications of neural network models of vision to test hypotheses about
visual representations in the brain
– transformer models for predicting fine-grained content labels in text
transcripts from therapy sessions
– improving transfer learning in neuroimaging
– fine-tuning of large language models for emulation of participants in
psychology experiments
 
Please emphasize this aspect of your experience in your application.
 
In general, we are seeking candidates who are capable of combining machine
learning, statistical, and domain-specific computational tools to solve
practical data analysis challenges (e.g. designing experiments, generating
and testing statistical hypotheses, training and interpreting predictive
models, and developing novel models and methods). Additionally, candidates
should be capable of visualizing and communicating findings to a broad
scientific audience, as well as explaining the details of relevant methods
to researchers in a variety of domains.
 
Other desirable experience includes:
 
– mathematical optimization (e.g. convex, linear programming, integer
programming)
– statistical inference (e.g. generalized linear models, mixed effect
models, state space models, survival analysis)
– reinforcement learning
– Bayesian statistical modelling
– other types of modelling of human/animal learning and decision-making
– neuroimaging data processing/ analysis (any MRI modality, MEG, or EEG)
– modelling of other types of neural data (e.g. neural recordings, calcium
imaging)
 
Finally, you should have demonstrable experience programming in languages
currently used in data-intensive, scientific computing, such as Python,
MATLAB or R. Experience with handling large datasets in high performance
computing settings is also very valuable. Although this position requires a
Ph.D. in a STEM discipline, we will consider applicants from a variety of
backgrounds, as their research experience is the most important factor.
Backgrounds of team members include computer science, statistics,
mathematics, and biomedical engineering.
 
This is an ideal position for someone who wants to establish a research
career in methods development and applications driven by scientific and
clinical needs. Given our access to a variety of collaborators and large or
unique datasets, there is ample opportunity to match research interests
with novel research problems. We also maintain collaborations outside of
the NIH, driven by our own research interests or community impact.
 
If you would like to be considered for this position, please send
francisco.pereira@nih.gov a CV, with your email serving as a cover letter.
We especially encourage applications from members of underrepresented
groups in the machine learning research community. If you already have a
research statement, please feel free to send that as well. There is no need
for reference letters at this stage. Other inquiries are also welcome.
Thank you for your attention and interest!

*Data scientist / ML engineer – ML for health, The Norwegian Centre for
Clinical Artificial Intelligence (SPKI), University Hospital of
North-Norway*
 

 
SPKI is expanding, and is in search of a highly motivated data scientist /
ML engineer who wants to contribute to the development and implementation
of new artificial intelligence (AI) tools for health.
 

 
The work will be done in a highly interdisciplinary environment, and you
will collaborate with a team consisting of clinicians, scientists from the
university and technologists, legal experts, industry partners, as well as
personnel responsible for ICT, data security, privacy concerns and more.
This environment also includes researchers at The Machine Learning Group
and Visual Intelligence .
 

 
For details, please see:
https://www.finn.no/job/fulltime/ad.html?finnkode=283131608

Poste : Ingénieur(e) de recherche NLP (CDI)

Présentation Emvista

Emvista transforme l’e-mail en un outil de productivité grâce à Prevyo, un assistant virtuel intelligent. Prevyo est capable de rediriger vos e-mails, de détecter ceux qui sont vraiment urgents, de classer les pièces jointes, d’enrichir votre carnet d’adresses. 

Emvista, entreprise située à Montpellier, est l’éditeur de Prevyo.

Prevyo repose sur une intelligence artificielle hybride qui combine les deux approches machine learning/deep learning et connaissances linguistiques et ontologiques. Cette IA analyse sémantiquement les e-mails en s’appuyant sur de nombreuses briques technologiques telles que la reconnaissance d’expressions temporelles, la reconnaissance d’entités nommées ou encore l’extraction d’événements. Ces informations sont représentées sous forme ontologique à partir de laquelle un raisonnement automatique est en mesure de faire apparaître des connaissances qui n’apparaissaient pas au premier abord.

Présentation du poste

Rattaché(e) directement au directeur de recherche pour renforcer l’équipe de R&D spécialisé dans les technologies du Traitement Automatique du Langage Naturel et de la Représentation des Connaissances, nous sommes à la recherche d’un(e) ingénieur(e) de recherche dans ces domaines.

Vous serez aussi en relation avec l’équipe de développement.

• CDI
• Télétravail partiel possible
• Montpellier

Missions 

La mission principale pour le ou la candidate est la prise en charge des travaux impliquant des techniques et modèles d’apprentissage (machine learning/deep learning) au sein des projets de Emvista. Dans ce cadre, les missions sont les suivantes :

• Contribution à la recherche et au développement des briques technologiques déjà existantes chez Emvista (parsing, normalisation, reconnaissance d’entités nommées, analyse d’opinions/émotions, résumé automatique, extraction de mots clés, génération de concepts, agent conversationnel, etc.) 
• Veille et état de l’art dans le domaine du NLP
• Evaluation des solutions NLP (académiques et industrielles)
• Encadrements d’étudiants (stagiaires, doctorants, etc.)
• Publications scientifiques (articles dans des conférences et journaux nationaux et internationaux, participation à des workshops, etc.)
• Vulgarisation de la recherche (articles journalistiques, réseaux sociaux, etc.)

Plus précisément, Emvista est coordinatrice d’un projet de recherche collaboratif intitulé POPCORN “Peuplement OPérationnel de bases de COnnaissances et Réseaux Neuronaux”. Ce projet subventionné par l’Agence de l’Innovation et de Défense (AID) implique trois partenaires : Emvista, Airbus Defense and Space et le Laboratoire d’Informatique de Grenoble (équipe GETALP). Le projet POPCORN aborde le problème de l’enrichissement semi-automatisé d’une base de connaissance via l’analyse automatique de textes. Le projet se focalise sur les trois axes de recherches suivants :

• Génération de données synthétiques textuelles à partir de textes de référence ;
• La reconnaissance des entités d’intérêt, des attributs associés et des relations entre les entités.
• La désambiguisation sémantique des entités (en cas d’homonymie par exemple)

POPCORN mobilisera plusieurs personnes de l’équipe R&D de Emvista dont vous qui aurez pour mission de prendre en charge les travaux impliquant du machine learning/deep learning appliqué au texte en collaboration avec les partenaires. La personne retenue sera pleinement investie dans POPCORN durant les 3 premières années qui correspondent à la durée du projet, à compter du 3 janvier 2022. Les résultats issus des recherches menées sur le projet POPCORN seront intégrés dans les solutions commercialisées de Emvista, dont Prevyo. Il s’agira notamment de structurer l’information contenue dans les e-mails (noms de projets, activités, clients, …) en vue de peupler une base d’un outil de management de relations clients (CRM).

Profil & Attitude

• Très bonne connaissance des algorithmes de machine learning pour le traitement automatique du langage naturel
• Maîtrise des modèles de langue récents, en particulier pour le français (BERT, FlauBERT, CamemBERT, …)
• Doctorat ou diplôme d’ingénieur avec spécialisation dans le Traitement Automatique du Langage Naturel
• Maîtrise des techniques et méthodologies de recherche
• Connaissance des nouvelles technologies NLP, des approches statistiques applicables au NLP
• Très bonne expression écrite en français (idéalement en anglais également)
• Être pédagogue
• De bonnes connaissances en Java, maîtrise de Python ainsi que des frameworks ML/DL (PyTorch, TensorFlow, Scikit-Learn …)

Informations complémentaires

Unité d’accueil : Emvista (groupe India Juliet)

Lieu : Espace Bocaud, 42 rue de la Pierre Plantée, 34830 Jacou

Merci d’envoyer votre candidature à cedric.lopez@emvista.com constitué du CV et d’une lettre de motivation.

Voici deux offres d’emploi pour des postes de Data Scientists que nous avons actuellement

• Offre Senior (au moins 3/4 ans d’expérience) :
  • https://careers.danone.com/en-global/jobs/senior-data-scientist-m-f-234686-en-us.html
• Offre Data Scientist (plus junior, mais ça peut être avec 1 an ou 2 d’expérience) :
  • https://careers.danone.com/en-global/jobs/data-scientist-m-f-232751-en-us.html