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PhD fellowship in Artificial Intelligence and Artificial Life

The appointments are for a term of 4 years, with 25% duties associated with education tasks (teaching, lab, supervision, etc.).

Modeling and implementation of self-organising computing systems based on biological neural networks, artificial neural networks, and cellular automata.

Keywords: artificial neural networks, biological neural networks, cellular automata, evolutionary algorithms, reservoir computing.

DeepCA is a multidisciplinary project including computer science and neuroscience, seeking radical breakthroughs toward the integration of biological and artificial neural systems.

The project aims at creating a theoretical and experimental foundation of a novel deep learning paradigm based on biological neural networks and cellular automata, by exploiting the computing substrates through evolutionary algorithms.

If you would like more information about the positon, feel free to contact: If you have technical questions about uploading the application please contact: Salary is set in accordance with the Norwegian State Salary Scale, job code 1017 NOK 449 400.

Applicants are kindly requested to send a diploma supplement or a similar document, which describes in detail the study and grading system and the rights for further studies associated with the obtained degree.

The application with a CV and certified copies of diplomas and certificates must be sent electronically via this page, with information about education and relevant experience (all in one combined PDF file).

Neural networks

With the help of neural networks—computer programs assembled from hundreds, thousands, or millions of artificial brain cells that learn and behave in a remarkably similar way to human brains.

Each neuron is made up of a cell body (the central mass of the cell) with a number of connections coming off it: numerous dendrites (the cell's inputs—carrying information toward the cell body) and a single axon (the cell's output—carrying information away).

the rest are glial cells, also called neuroglia, that support and protect the neurons and feed them with energy that allows them to work and grow.) Inside a computer, the equivalent to a brain cell is a nanoscopically

The transistors in a computer are wired in relatively simple, serial chains (each one is connected to maybe two or three others in basic arrangements known as logic gates), whereas the neurons in a brain are densely interconnected in complex, parallel ways (each one is connected to perhaps 10,000 of its neighbors).

This essential structural difference between computers (with maybe a few hundred million transistors connected in a relatively simple way) and brains (perhaps 10–100 times more brain cells connected in richer and more complex ways) is what makes them 'think' so very differently.

But, unlike computers, they can spontaneously put information together in astounding new ways—that's where the human creativity of a Beethoven or a Shakespeare comes from—recognizing original patterns, forging connections, and seeing the things they've learned in a completely different light.

The basic idea behind a neural network is to simulate (copy in a simplified but reasonably faithful way) lots of densely interconnected brain cells inside a computer so you can get it to learn things, recognize patterns, and make decisions in a humanlike way.

It's important to note that neural networks are (generally) software simulations: they're made by programming very ordinary computers, working in a very traditional fashion with their ordinary transistors and serially connected logic gates, to behave as though they're built from billions of highly interconnected brain cells working in parallel.

Strictly speaking, neural networks produced this way are called artificial neural networks (or ANNs) to differentiate them from the real neural networks (collections of interconnected brain cells) we find inside our brains.

You might also see neural networks referred to by names like connectionist machines (the field is also called connectionism), parallel distributed processors (PDP), thinking machines, and so on—but in this article we're going to use the term 'neural network' throughout and always use it to mean 'artificial neural network.'

Photo: A fully connected neural network is made up of input units (red), hidden units (blue), and output units (yellow), with all the units connected to all the units in the layers either side.

When it's learning (being trained) or operating normally (after being trained), patterns of information are fed into the network via the input units, which trigger the layers of hidden units, and these in turn arrive at the output units.

Every unit adds up all the inputs it receives in this way and (in the simplest type of network) if the sum is more than a certain threshold value, the unit 'fires' and triggers the units it's connected to (those on its right).

This involves comparing the output a network produces with the output it was meant to produce, and using the difference between them to modify the weights of the connections between the units in the network, working from the output units through the hidden units to the input units—going backward, in other words.

So, during the learning phase, the network is simply looking at lots of numbers like 10110 and 01001 and learning that some mean chair (which might be an output of 1) while others mean table (an output of 0).

In airplanes, you might use a neural network as a basic autopilot, with input units reading signals from the various cockpit instruments and output units modifying the plane's controls appropriately to keep it safely on course.

You could measure the final detergent in various ways (its color, acidity, thickness, or whatever), feed those measurements into your neural network as inputs, and then have the network decide whether to accept or reject the batch.

In a very similar way, a bank could use a neural network to help it decide whether to give loans to people on the basis of their past credit history, current earnings, and employment record.

Each character (letter, number, or symbol) that you write is recognized on the basis of key features it contains (vertical lines, horizontal lines, angled lines, curves, and so on) and the order in which you draw them on the screen.

They can help us forecast the stockmarket or the weather, operate radar scanning systems that automatically identify enemy aircraft or ships, and even help doctors to diagnose complex diseases on the basis of their symptoms.

If you use cellphone apps that recognize your handwriting on a touchscreen, they might be using a simple neural network to figure out which characters you're writing by looking out for distinct features in the marks you make with your fingers (and the order in which you make them).

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