The selected candidate will be part of the UCADatalab group and has the option to enroll on a Doctorate Program at Universidad de Cádiz, using part of his work towards completion of a PhD.

Qualification: BSc/MSc in Computer Science or equivalent studies.

Experience: Proven experience as a web developer

Required technologies:

– Django Framework (Python)

– Database management: MySQL or Postgre

– Front-end technologies: HTML5, CSS3, Javascript, Bootstrap/Foundation/SemanticUI or similar, JQuery, Json, AJAX

Desirable technologies:

– Additional back-end languages (Java, NodeJS, PHP)

– Additional front-end technologies (e.g. AngularJS)

– NoSQL databases (MongoDB, Neo4J, HBase)

– Development and deployment of RESTful architectures (DJango Rest Framework, Express, Jersey)

– Hadoop ecosystem

– UNIX system management

Salary: 1633 Eur/month (before tax). 2000 Eur yearly bonus by incentives.

Interested candidates are encouraged to send their CV to datalab@uca.es.

The selected candidate will be part of the UCADatalab group and has the option to enroll on a Doctorate Program at Universidad de Cádiz, using part of his work towards completion of a PhD.

Qualification: BSc/MSc in Mathematics, Physics or Engineering.

Required skills:

• knowledge of python libraries for data science & machine learning (pandas, numpy, seaborn, scikit-learn)
• data crunching and exploratory data analysis
• supervised learning: classification and regression models.
• visualization tools in python environments (matplotlib, seaborn, plotly, bokeh, etc.)

Desirable skills:

• Bayesian models in statistics
• Deep Learning frameworks (torch, keras, tensorflow)
• Time series forecasting
• Big data ecosystems

Salary: 1633 Eur/month (before tax). 2000 Eur bonus by incentives.

Interested candidates are encouraged to send their CV to datalab@uca.es.

Nowadays, most of people are living in a dynamic world where things and humans are unavoidable interconnected. Some real examples of such a kind of fully connected worlds are the smart cities or the smart grids which are in turn supported by underlying technological architectures and devices. In this types of scenarios, the ad hoc networks suit well due to the special characteristics being, however, the last ones the main cause of their security weaknesses. For example, the inherent decentralized architecture and self-management characteristics make them vulnerable to identity spoofing (sybil), data manipulation or fabrication (data tampering) or dropping attacks, among others. Depending on the context of application, the previous attacks could affect not only on the services offered by the system but on humans lives.

In this context, security solutions based on the use of prevention, detection or response/tolerance mechanisms have been proposed, being the last ones less frequent. It is a fact that most of them are focused on fighting against certain types of attacks and usually acts on specific parameters and/or characteristics of the involved system. This approach, on one hand, makes the proposals more robust and effective for the threats they were designed to but, on the other hand, they are definitively less resilient/effective when new security threats or attacks appear. In the context of the security in information and communication technology (ICT) such kind of attacks are known as zero-day attacks.

All the previous reasons make necessary the proposal of novel security solutions and mechanisms addressing large amount of requirements, characteristics and parameters from a global point of view. All together, will provide more robust and resilient systems, in other words, survivable systems against unknown security threats or attacks.

Certainly, it is a great and open research challenge for the security on ICT systems that would add the necessary capabilities in the mitigation of the impact of almost all attacks on the system where they were deployed.

Candidate Profile

Al finalizar esta tesis, el/la candidato/candidata adquirirá tanto una formación académica y tecnológica así como una experiencia profesional en uno de los más demandados campos de aplicación actual, como es la Industria 4.0

• simple methods for supervised learning: Random Forest, Factorization Machines, SVMs, etc.
• deep learning (LSTMs, Conv-Nets)
• forecasting in time series (DLMs, Kalman filters).
• Bayesian methods
• techniques for data cleaning, missing data imputation and visualization.

The candidate will tackle applied ML projects on computer vision (object detection, image classification), time series forecasting or Natural Language Processing, with special emphasis on their application to industrial problems.

Candidate Profile

Candidate Profile