NLP postions at DeepCognito

DeepCognito Ltd. is recruiting:

  • 2 x NLP/AI Research Engineers
  • 1 x NLP Software Engineer
  • 1 x Technical Project Manager

We’ll consider graduate to senior level applicants (including part-time
roles for PhD students).

Successful applicants will participate in a multi-million pound project
developing and applying state-of-the-art NLP/AI methodologies to clinical
electronic health records across secondary and tertiary NHS hospitals in
the United Kingdom. We offer a competitive salary, pension, gym membership,
healthy breakfast and snacks, bespoke career progression routes, and the
opportunity to be involved in real-world projects with immediate impact.

Due to the current sensitivity surrounding these projects we cannot
advertise publicly. If you want to hear more, please reach out by email
(recruitment@deepcognito.com) and include your CV.
Posted in October 2018.


PhD study: using EHR to improve identification of ADRs in veterinary medicine

An exciting opportunity to study for PhD in “Using electronic health records to improve identification and reporting of adverse drug reactions in veterinary medicine” at the Unverisity of Liverpool. Train in the fields of big health data analysis, epidemiology and if you wish machine learning and computer science, as part of this novel and exciting project aimed at improving the health of UK pets through better detection of adverse drug reactions.

Details here.

Closing date for applications: 23th October 2018

Posted in October 2018.


NLP and health data science posts at King’s

We are recruiting to four NLP and health data science posts at King’s College London. Successful applicants will apply cutting edge AI and NLP to electronic health record projects, joining a team of computational linguists, data scientists, informaticians, epidemiologists and clinicians at the world leading Institute of Psychiatry, Psychology and Neuroscience.

Full details and instructions for applying can be found here:

https://www.jobs.ac.uk/job/BMV771/post-doctoral-researcher
https://www.jobs.ac.uk/job/BMV785/post-doctoral-researcher
https://www.jobs.ac.uk/job/BMV793/pre-doctoral-researcher-in-clinical-informatics
https://www.jobs.ac.uk/job/BMV298/systems-engineer

Posted in September 2018.


 

PhD Studentship – Deep Learning for Data Analytics and/or Artificial Intelligence

The University of Hull invites applications for a fully-funded PhD scholarship in the area of deep learning and data analytics or artificial intelligence. Particular areas of interest are interactive systems and natural language processing, but invitations in any area of AI or evolutionary algorithms are invited.

See details here.

Posted in July 2018.


Mining health information on the Social Web – towards an understanding of the influence of social media on public healthcare

This is a fully funded PhD studentship available through the Information School, University of Sheffield. The project will develop natural language processing methods to mine health related facts mentioned from heterogeneous social media resources, and integrate and link them in a structured Knowledge Graph (KG). The KG will capture the health information on the social Web in a structured way, facilitating complex, structured queries and ultimately enabling the first crucial steps towards a quantitative and qualitative analysis of health information from the social Web.

This is a fully funded (tuition + stipend) 3.5 years PhD studentship with an excellent opportunity to develop knowledge and experience in the area of health informatics, working with well known scholar such as Professor Peter Bath. Also that PhD experience is usually counted as real work experience. Please read carefully the ‘Eligibility’ section before you apply, because EPSRC typically only funds UK and EU (with constraints) students.

See details here.

Posted in June 2018.


PhD at Swansea University, UK: Health informatics, machine learning, text analytics, natural language processing, public health, epidemiology

Healthcare systems have collected mountains of textual and numeric patient records about disease activities, hospital admissions and visits, drug prescriptions, physician notes and more. But medical research and related industries like pharmaceutical industry are facing with enormous challenges as a result of the very restrictive handling of such health data.

This PhD studentship offers an exciting opportunity of exploring and /or developing machine learning, natural language processing, text analytics techniques to extract valuable knowledge from SNOMED CT derived clinical narratives. Such knowledge will enable better care, the prognosis of patients, promotion of clinical and research initiatives, fewer medical errors and lower costs, and thus a better patient life.

This project will involve industrial collaboration with the Clinithink Ltd. You will has the chance of working in a very dynamic academic research environments offered by the world-class UK Farr Institute of Health Informatics Research (http://www.farrinstitute.org/). We make up one part of this Institute – CIPHER (The Centre for Improvement in Population Health through E-records Research). You will be supervised by Professor Ronan Lyons, Dr Shang-Ming Zhou and Mr Phil Davies.

The successful candidate is expected to start the PhD scholarship in January 2018.

Scholarships are collaborative awards with external partners including SME’s and micro companies, as well as public and third sector organisations. The scholarship provides 3 years of funding with a 6 month period to complete the thesis. The achievement of a postgraduate skills development award, PSDA, is compulsory for each KESS II scholar and is based on a 60 credit award.

Eligibility: This PhD Scholarship is offered for UK or EU applicants, or applicants with Indefinite Leave to Remain in the UK. Applicants should have a minimum of a 2.1 undergraduate degree and/or a master’s degree (or equivalent qualification) in the Computer science, Computational linguistics, Computing, Data Science, Statistics, Epidemiology, Health informatics, Medical Informatics, Bioinformatics, or any areas related.

Funding: The studentship covers the full cost of UK/EU tuition fees, plus a stipend. The bursary will be limited to a maximum of £14,198 p.a. dependent upon the applicant’s financial circumstances as assessed in section C point 4 on the KESS II participant proposal form. There will also be additional funds available for research expenses.

How to Apply: Applicants are advised to contact Dr Shang-Ming Zhou regarding information on the area of research, by email or by telephone: (s.zhou@swansea.ac.uk / +44 (0)1792 602580). Please go to this link to submit the application:

For any other queries, please contact: KESSstudentenquiries@swansea.ac.uk

Posted in May 2018.


Research Associate – text mining electronic health records, University of Edinburgh

Applications are invited for a Research Associate position on text mining of electronic health records in the Institute for Language, Cognition and Computation at the School of Informatics, University of Edinburgh.

The position is part of study of a wider project in collaboration with the Centre for Clinical Brain Sciences on “Leveraging routinely collected and linked research data to study the causes and consequences of common mental disorders” funded by MRC (Mental Health Pathfinder Award). The study will mine neuroimaging data held in electronic health records to investigate the relationship between mental health and recovery from common medical and neurological conditions. It will determine whether there are systematic neuroimaging differences between those with and without psychiatric symptoms, focusing on post-stroke depression as an exemplar model for other disorders.

Text mining methods will be researched and developed to derive information from radiologist reports about CT and MRI brain imaging and link it to mental health outcomes. This work will use and build on an existing text mining system developed for unstructured neuroimaging reports of patients who suffered from stroke. The text mining involves four main tasks: named entity recognition, relation extraction, negation detection and document labelling. The project will investigate using machine learning methods for these tasks and evaluating their performance against existing rule-based methods.

The position will be an opportunity to conduct research on deep learning using neural networks within the wider context of health care text analytics and data science.

More information can be found here.

Please feel free to contact Beatrice Alex with any enquiries (balex@staffmail.ed.ac.uk).

Posted in March 2018.


Postdoctoral Researcher – Knowledge based technologies

IBM Research in the UK is seeking a high quality postdoctoral researcher to work at the Daresbury research facility in collaboration with the Science and Technology Facility Council’s (STFC) Hartree Centre.

The successful candidate will join the IBM Research UK team at Daresbury Laboratory, which aims to have tangible business impact in the UK industry through cutting edge research in technologies and applications—especially by implementing next generation cognitive solutions. In order to support this endeavour, you will have experience in the intersection of advanced research into natural language processing (NLP), knowledge representation and reasoning (KRR) and machine learning (ML). The present target of our research is the areas of life sciences, chemistry, engineering and manufacturing through information mining, thus candidates with experience in NLP, KRR, and ML in support of any of these areas are encouraged to apply.

More details: here.

Posted in February 2018.


NLP Research Engineer

DeepCognito are looking for a Natural Language Processing (NLP) Engineer with in-depth knowledge and experience of text analytics methods to join their Research and Development team for their flagship product AITAP. This role will be based in Manchester Science Park.

You will join a small and dynamic team with ambitious vision of AI in the text analytics space that work on exciting real-world problems through research, development and application of state-of-the-art technologies and methodologies.

This is a fantastic opportunity for a research engineer with practical experience of NLP techniques to join an exciting data science start-up. If you have all the skills and are highly motivated, creative, organised and proactive, we would love to hear from you!

More details: here.

Posted in January 2018.


The UKRI Innovation/Rutherford Fund Fellowships at HDR UK

The University of Manchester, on behalf of the MRC and HDR UK, is looking to recruit to two fellowships that will work at the centre of health data science research, focusing on one of the national priorities defined by the HDR UK programme:

  • Actionable Analytics
  • 21st Century Clinical Trials
  • Data-Driven Public Health
  • Precision Medicine.

The fellowships will provide three years of personal support (salary and research costs).  Fellows are expected to be ‘rising stars’ at the early to mid-career stage (non-clinical), in line with potential candidates for MRC Skills Development Fellowships/Career Development Awards. Fellowships are aimed at researchers and technology specialists outside of the traditional academic PI track.

  • Early career stage fellowships are aimed at post-doctoral candidates that have experience of delivering previous research projects with some evidence of outputs (eg publications).
  • Mid-career stage fellowships are aimed at those post-doctoral candidates wishing to make a transition into independence with some evidenced productivity across past appointments on an upwards trajectory.

Due to the funding requirements of the post, applicants must be available to commence the role no later than 15 February 2018.

Deadline for applications: 22nd December 2017

For full details of the award, and to download the application form please visit:

https://www.bmh.manchester.ac.uk/research/support/fellowships/mrc-hdruk/

Posted in December 2017.


KESS II Funded PhD Scholarship in Healthcare Data Analytics and Text Mining, Swansea University, UK

 

Subject study:

Health informatics, machine learning, text analytics, natural language processing, public health, epidemiology

Key Information

Healthcare systems have collected mountains of textual and numeric patient records about disease activities, hospital admissions and visits, drug prescriptions, physician notes and more. But medical research and related industries like pharmaceutical industry are facing with enormous challenges as a result of the very restrictive handling of such health data.

This PhD studentship offers an exciting opportunity of exploring and /or developing machine learning, natural language processing, text analytics techniques to extract valuable knowledge from SNOMED CT derived clinical narratives. Such knowledge will enable better care, prognosis of patients, promotion of clinical and research initiatives, fewer medical errors and lower costs, and thus a better patient life.

This project will involve industrial collaboration with the Clinithink Ltd.

You will has the chance of working in a very dynamic academic research environments offered by the world class UK Farr Institute of Health Informatics Research (http://www.farrinstitute.org/). We make up one part of this Institute – CIPHER (The Centre for Improvement in Population Health through E-records Research) http://www.swansea.ac.uk/medicine/research/researchthemes/patientpopulationhealthandinformatics/ehealth-and-informatics-research/thefarrinstitutecipher/

You will be supervised by Professor Ronan Lyons, Dr Shang-Ming Zhou and Mr Phil Davies.

The successful candidate is expected to start the PhD scholarship in January 2018.

Scholarships are collaborative awards with external partners including SME’s and micro companies, as well as public and third sector organisations. The scholarship provides 3 years of funding with a 6 month period to complete the thesis. The achievement of a postgraduate skills development award, PSDA, is compulsory for each KESS II scholar and is based on a 60 credit award.

 
Eligibility

This PhD Scholarship is offered for UK or EU applicants, or applicants with Indefinite Leave to Remain in the UK.

Applicants should have a minimum of a 2.1 undergraduate degree and/or a master’s degree (or equivalent qualification) in the Computer science, Computational linguistics, Computing, Data science, Statistics, Epidemiology, Health informatics, Medical Informatics, Bioinformatics, or any areas related.

Funding

The studentship covers the full cost of UK/EU tuition fees, plus a stipend.  The bursary will be limited to a maximum of £14,198 p.a. dependent upon the applicant’s financial circumstances as assessed in section C point 4 on the KESS II participant proposal form

There will also be additional funds available for research expenses.

How to Apply

Applicants are strongly advised to contact Dr Shang-Ming Zhou regarding information on the area of research, by email or by telephone: (s.zhou@swansea.ac.uk / +44 (0)1792 602580).

Please go to the link below to submit the application:

http://www.swansea.ac.uk/postgraduate/scholarships/research/health-informatics-kess-phd-healthcare-data-analytics.php

For any other queries, please contact: KESSstudentenquiries@swansea.ac.uk

Closing Date:

Applicants should apply for this scholarship as soon as possible.


Research Associate – Text mining of veterinary records

This is an exciting opportunity for an experienced and enthusiastic text mining  researcher to work on the SAVSNET project, which collects and analyses free-text narrative contained in health records of pets in the UK. SAVSNET have already developed a series of clinical text mining algorithms to extract clinical signs and parameters recorded in the veterinary clinical narrative, and the current project (funded by BBSRC) aims to consolidate the existing and develop new algorithms that are able to identify key details and context of each patient’s visit in near-real time. The successful candidate will be involved in designing, consolidating and implementing both rule-based and data-driven approaches, and their integration within the existing SAVSNET platform.

Details: http://www.jobs.ac.uk/job/BCD641/research-associate-text-mining-of-veterinary-records/

Closing Date : 16/07/2017
Duration : Available from Sept 2017 for up to 18 months full-time (or equivalent part-time) in the first instance;
School/Directorate: School of Computer Science, University of Manchester
Job Reference: S&E-010235


Two NLP research posts at King’s College

We are pleased to announce two exciting job opportunities for postdoctoral research in NLP and Clinical Informatics at King’s College, London:

* One 2-year Research Associate post with a focus on development of methods using anonymised electronic health record (EHR) data for research into suicide risk as part of a wider research programme entitled Electronic health records to predict HOspitalised Suicide attempts: Targeting Information Technology solutions (e-HOST-IT):

https://www.hirewire.co.uk/HE/1061247/MS_JobDetails.aspx?JobID=77228

* One 1-year Research associate post with a focus on development of solutions for extracting semantic (in particular temporal) information from EHR text in the mental health domain as part of the research project Measuring Duration of Untreated Psychosis by Extraction of Symptom and Treatment Onset from mental health records using language technology (MeDESTO):

https://www.hirewire.co.uk/HE/1061247/MS_JobDetails.aspx?JobID=77217
Please don’t hesitate to contact us if you have any questions (Sumithra & Rina).


Research Associate in Clinical Informatics

Reference: THW/17/059639/000827
Salary Details: £32,958 – £39,324 per annum
Allowances: plus £2,623 London Allowance
Contract Type: Temporary/Fixed term
Contract Term: Full time

We have an exciting opportunity for a research associate to join our Informatics team (http://core.brc.iop.kcl.ac.uk) at the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at King’s College London (www.slam.nhs.uk/brc).

The team works in close collaboration with researchers and clinicians from across the King’s Health Partners Academic Health Sciences Centre (http://www.kingshealthpartners.org/), the GATE team at the University of Sheffield (GATE, http://gate.ac.uk) and the Healthcare Text Analytics Group at Manchester University (http://gnode1.mib.man.ac.uk/hecta.html), as well as other international research groups in clinical Natural Language Processing.

The team is involved in developing a variety of leading world research programmes at King’s Health Partners including the NIHR Bioresource and the Clinical Records Interactive Search (CRIS) system at South London and Maudsley NHS Foundation Trust, an innovative resource built by the Maudsley BRC which allows researchers access to de-identified NHS electronic health records data to enhance clinical research for patient benefit.

The post holder will work as part of a wider research project entitled Electronic health records to predict Hospitalised Suicide attempts: Targeting Information Technology solutions (e-HOST-IT). Advanced programming, text data manipulation and analysis skills, e.g. in python, Java or similar, are required.

Positioned at the University/NHS Trust interface the posts offer an exciting opportunity to work with an internationally leading multi-disciplinary team of researchers and clinicians to accelerate advances in translational clinical research and experimental medicine and transform mental and physical healthcare through the power of big data.

The selection process will include competency based questions, a presentation, an assessment and a panel interview.

To apply for this role, please go to the King’s College London HireWire Job Board and register to download and submit the specified application form.

Closing date: 04 August 2017

If you have questions about this role, please contact: Dr Rina Dutta, Email: rina.dutta@kcl.ac.uk,

Application details here.