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Particularly in demand are biologists with expertise in statistics, mathematical modeling, machine learning, artificial intelligence, software engineering, or database design.
Indeed, pharma and biotechnology companies see an increasing demand for computational scientists with strong skills in mathematical modeling, machine learning, data mining, complex optimization and data representation (e.g., [ 3]).
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The novel work (Forsberg et al. 2015), published in this issue of EBioMedicine, utilized complex mathematical modeling using machine learning techniques to convert non-predictive individual markers into a useful pool construct with both high sensitivity and high specificity for predicting wound closure.
In particular, nonlinear mathematical models in machine variables as well as in an arbitrary reference frame are found in Cauchy's form, and the motion control problem is studied for induction motors from closed-loop perspectives.
The forecasting approaches included statistical modelling, mathematical modelling and machine learning methods.
Providing simulations of mathematical models of machines derived from initial design conditions and comparing these to actual machine conditions during production operation accessed via the Internet, enables professionals to remotely and economically determine the current operating status of these machines thereby detecting adverse trending potentially leading to machine failures.
Nowadays, the availability of reliable mathematical models of machining system dynamics is a key issue for achieving high quality standards in precision machining.
Different optimization techniques have been used so far to solve the mathematical models for machining problems.
The forecasting approaches included statistical modelling, mathematical modelling and machine-learning methods (table 2).
By means of a mathematical model of the machine, the mechanical behavior of the machine itself and hence the range figures of possible projectiles are computed.
Liao et al. (1997) established mathematical models relating the machining performances with various machining parameters, and then determined the optimal parametric settings for the WEDM processes using feasible-direction method of non-linear programining.
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